The DOI matches its Crossref record; 10 of the 12 references it cites were found in a registry or an index.
A Performance Evaluation of Federated Learning Algorithms
Open accessAbstract
Federated learning is an approach to distributed machine learning where a global model is learned by aggregating models that have been trained locally on data-generating clients. Contrary to centralized optimization, clients can be very large in number and face challenges of data and network heterogeneity. Examples of clients include smartphones and connected vehicles, which highlights the practical relevance of federated learning. We benchmark three federated learning algorithms and compare their performance against a centralized approach where data resides on the server. The algorithms Federated Averaging (FedAvg), Federated Stochastic Variance Reduced Gradient, and CO-OP are evaluated on the MNIST dataset, using both i.i.d. and non-i.i.d. partitionings of the data. Our results show that FedAvg achieves the highest accuracy among the federated algorithms, regardless of how data was partitioned. Our comparison between FedAvg and centralized learning shows that they are practically equivalent when i.i.d. data is used. However, the centralized approach outperforms FedAvg with non-i.i.d. data.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [18]
In the citing paper
“These algorithms were evaluated on MINIST dataset with the use of non-i.i.d. and i.i.d.partitioning of data.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [125]
In the citing paper
“Nilsson et al. benchmarked three FL algorithms.They compared the performance of these three algorithms by residing the data on the server [125].The algorithms include federated averaging (FedAvg), CO-OP, and Federated-Stochastic Variance Reduced Gradient.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [125]
In the citing paper
“The research resulted in FedAvg as the highest accuracy algorithm among all.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [125]
In the citing paper
“FedAvg achieves the highest accuracy of 98% among these algorithms, regardless of the manner of data partitioning.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [125]
Citations
Cites
(2016) · Cited by 11,679
(2012) · Cited by 8,009
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016) · arXiv · doi:10.48550/arxiv.1610.02527 · Cited by 1,563
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysisPreprint
Benavoli, A., Giorgio Corani, A., Demšar, J., & Marco Zaffalon, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04316 · Cited by 324
Distance metric learning with eigenvalue optimization
Ying, Y., & Li, P. (2012) · Open Research Exeter (University of Exeter) · Cited by 301
On the Way towards Fourth-Generation Mobile: 3GPP LTE and LTE-Advanced
Martín‐Sacristán, D., Monserrat, J. F., Cabrejas-Peñuelas, J., Calabuig, D., Garrigas, S., & Cardona, N. (2009) · EURASIP Journal on Wireless Communications and Networking · doi:10.1155/2009/354089 · Cited by 169
A Bayesian approach for comparing cross-validated algorithms on multiple data sets
Corani, G., & Benavoli, A. (2015) · Machine Learning · doi:10.1007/s10994-015-5486-z · Cited by 70
Big Automotive Data: Leveraging large volumes of data for knowledge-driven product development
Johanson, M., Belenki, S., Jalminger, J., Fant, M., & Gjertz, M. (2014) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2014.7004298 · Cited by 59
KELP: a Kernel-based Learning Platform
Filice, S., Castellucci, G., Martino, G. D. S., Aless, Moschitti, R., Croce, D., & Basili, R. (2018) · Cineca Institutional Research Information System (Tor Vergata University) · Cited by 48
Is LTE-Advanced really advanced?
Hyun, J., Won, Y., Kim, E., Yoo, J., & Hong, J. W. (2016) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2016.7502881 · Cited by 2
Research works printed but not linked: 3
- Hitachi Data Systems . 2015. The Internet on Wheels and Hitachi , Ltd . White Paper. Hitachi Data Systems. 2015. The Internet on Wheels and Hitachi, Ltd. White Paper.
- H Brendan McMahan Eider Moore Daniel Ramage Seth Hampson etal 2016. Communication-efficient learning of deep networks from decentralized data. arXiv:1602.05629 H Brendan McMahan Eider Moore Daniel Ramage Seth Hampson et al. 2016. Communication-efficient learning of deep networks from decentralized data. arXiv:1602.05629 · arXiv
- Yushi Wang . 2017. CO-OP: Cooperative Machine Learning from Mobile Devices. Master's thesis. Dept. Elect. and Comput. Eng ., Univ. Alberta , Edmonton, Canada . Yushi Wang. 2017. CO-OP: Cooperative Machine Learning from Mobile Devices. Master's thesis. Dept. Elect. and Comput. Eng., Univ. Alberta, Edmonton, Canada.
Cited by
Sorted by citations
Items 101–200 of 328
Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System
Holly, S., Hießl, T., Lakani, S. R., Schall, D., Heitzinger, C., & Kemnitz, J. (2022) · Springer eBooks · doi:10.1007/978-3-658-36295-9_1 · Cited by 18
Arasteh, S. T., Ríos-Urrego, C. D., Nöth, E., Maier, A., Yang, S. H., Rusz, J., & Orozco‐Arroyave, J. R. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-2108 · Cited by 22
Ooi, M. P., Sohail, S., Huang, V., Hudson, N., Baughman, M., RANA, O. F., Hinze, A., Chard, K., Chard, R., Foster, I. T., et al. (2023) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2023.10328671 · Cited by 20
A Privacy-Preserving Collaborative Federated Learning Framework for Detecting Retinal Diseases
Gulati, S., Guleria, K., Goyal, N., AlZubi, A. A., & Castilla, Á. K. (2024) · IEEE Access · doi:10.1109/access.2024.3493946 · Cited by 21
A Review and Experimental Evaluation on Split Learning
Hu, Z., Zhou, T., Wu, B., Chen, C., & Wang, Y. (2025) · Future Internet · doi:10.3390/fi17020087 · Cited by 19
Singh, J. P., Aqsa, A., Ghani, I., Sonani, R., & Govindarajan, V. (2025) · Future Internet · doi:10.3390/fi17080345 · Cited by 25
Privacy protected internet of unmanned aerial vehicles for disastrous site identification
Khullar, V., & Singh, H. P. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7040 · Cited by 16
Dimolianis, M., Kalogeras, D., Kostopoulos, N., & Maglaris, V. (2022) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet55617.2022.9978815 · Cited by 16
Towards flexible data stream collaboration: Federated Learning in Kafka-ML
Chaves, A. J., Martín, C., & Díáz, M. (2024) · Internet of Things · doi:10.1016/j.iot.2023.101036 · Cited by 18
Wardana, A. A., Sukarno, P., & Salman, M. (2024) · International Conference on Software and Computer Applications (ICSCA) · doi:10.1145/3651781.3651825 · Cited by 17
Grundzüge des maschinellen Lernens
Lanquillon, C. (2019) · Springer eBooks · doi:10.1007/978-3-662-60408-3_3 · Cited by 16
OODIDA: On-Board/Off-Board Distributed Real-Time Data Analytics for Connected Vehicles
Ulm, G., Smith, S., Nilsson, A., Gustavsson, E., & Jirstrand, M. (2021) · Data Science and Engineering · doi:10.1007/s41019-021-00152-6 · Cited by 16
Self-citationLi, Q., Zhu, W., Wu, C., Pan, X., Yang, F., Zhou, Y., & Zhang, Y. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413923 · Cited by 16
Shaheen, M., Farooq, M. S., & Umer, T. (2023) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan13010001 · Cited by 15
Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images
Das, B. C., Amini, M. H., & Wu, Y. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385829 · Cited by 17
Toward Ethical AI: A Qualitative Analysis of Stakeholder Perspectives
Shrestha, A. K., & Joshi, S. (2025) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc62904.2025.10903879 · Cited by 15
End-to-End Verifiable Decentralized Federated Learning
Lee, C., Heiss, J., Tai, S., & Hong, J. W. (2024) · IEEE International Conference on Blockchain and Cryptocurrency (ICBC) · doi:10.1109/icbc59979.2024.10634412 · Cited by 14
A Remedy for Heterogeneous Data: Clustered Federated Learning with Gradient Trajectory
Liu, R., Yu, S., Lan, L., Wang, J., Kant, K., & Calleja, N. (2024) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020065 · Cited by 15
Gaber, A., Abdeltwab, H., & Elbatt, T. (2024) · International Conference on Machine Learning and Soft Computing · doi:10.1145/3647750.3647752 · Cited by 13
Enabling efficient and low-effort decentralized federated learning with the EdgeFL framework
Zhang, H., Bosch, J., & Olsson, H. H. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107600 · Cited by 19
Cowlishaw, R., Longépé, N., & Riccardi, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-94244-2 · Cited by 14
Towards Federated Learning in Edge Computing for Real-Time Traffic Estimation in Smart Cities
da Silva, M. V. S., Bittencourt, L. F., & Rivera, A. R. (2020) · Anais do Workshop de Computação Urbana (CoUrb) · doi:10.5753/courb.2020.12361 · Cited by 13
Accuracy Rate Maximization in Edge Federated Learning With Delay and Energy Constraints
Yuan, P., Huang, R., Zhang, J., Zhang, E., & Zhao, X. (2022) · IEEE Systems Journal · doi:10.1109/jsyst.2022.3203727 · Cited by 13
Husnoo, M. A., Anwar, A., Reda, H. T., Hosseinzadeh, N., Islam, S. N., Mahmood, A. N., & Doss, R. (2023) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps57453.2023.10226030 · Cited by 13
Federated ViT: A Distributed Deep Learning Framework for Skin Cancer Classification
Junejo, R. H., Abbas, Q., Awais, M., Akram, T., & Aldajani, M. B. (2025) · IEEE Access · doi:10.1109/access.2025.3612477 · Cited by 12
IDS-MA: Intrusion Detection System for IoT MQTT Attacks Using Centralized and Federated Learning
Omotosho, A., Qendah, Y., & Hammer, C. (2023) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac57700.2023.00093 · Cited by 13
Confidentiality Preserved Federated Learning for Indoor Localization Using Wi-Fi Fingerprinting
Kumar, R., Popli, R., Khullar, V., Kansal, I., & Sharma, A. (2023) · Buildings · doi:10.3390/buildings13082048 · Cited by 12
Enhancing Decentralized and Personalized Federated Learning With Topology Construction
Chen, S., Xu, Y., Xu, H., Ma, Z., & Wang, Z. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3367872 · Cited by 12
Lightweight federated learning for STIs/HIV prediction
Nguyen, T. P. V., Yang, W., Tang, Z., Xia, X., Mullens, A. B., Dean, J. A., & Li, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-56115-0 · Cited by 12
Physics-guided federated learning as an enabler for digital twins
Stadtmann, F., Furevik, E. R., Rasheed, A., & Kvamsdal, T. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125169 · Cited by 12
Surveying federated learning approaches through a multi-criteria categorization
Caruccio, L., Cimino, G., Deufemia, V., Iuliano, G., & Stanzione, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16050-0 · Cited by 11
A Beginner's Guide to Federated Learning
Reddy, G. P., & Kumar, Y. V. P. (2023) · Intelligent Methods, Systems, and Applications (IMSA) · doi:10.1109/imsa58542.2023.10217383 · Cited by 11
Al-Naday, M., Dobre, V., Reed, M. J., Salman Zubair, T. O. O. R., Volckaert, B., & Turck, F. D. (2023) · Annals of Telecommunications · doi:10.1007/s12243-023-00977-4 · Cited by 16
2P3FL: A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning
Dasari, S., & Kaluri, R. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.049152 · Cited by 14
Xu, H., Fan, Z., & Liu, X. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108760 · Cited by 11
Optimized Active Fuzzy Deep Federated Learning for predicting autism spectrum disorder
Daliri, A., Khalilian, M., Mohammadzadeh, J., & Hosseini, S. S. (2025) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-025-00523-3 · Cited by 11
Zhu, R., Li, M., Liu, H., Liu, L., & Ma, M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3179442 · Cited by 13
Federated Learning of Deep Neural Decision Forests
Sjöberg, A., Gustavsson, E., Koppisetty, A. C., & Jirstrand, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-37599-7_58 · Cited by 11
Self-citationAnalysis of Federated Learning as a Distributed Solution for Learning on Edge Devices
Lameh, S. F., Noble, W., Amannejad, Y., & Afshar, A. (2020) · International Conference on Intelligent Data Science Technologies and Applications (IDSTA) · doi:10.1109/idsta50958.2020.9264060 · Cited by 11
Roy, A., & Dasgupta, D. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3386500 · Cited by 10
A privacy preserving multi-center federated learning framework for district heating forecast
Dai, K., González, E. F., & García-Betances, R. I. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.115164 · Cited by 10
Where is the Testbed for My Federated Learning Research?
Božič, J., Faustino, A. R., Radovič, B., Canini, M., & Pejović, V. (2024) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec62691.2024.00027 · Cited by 13
Rezaie, H., Taheri, R., Shojafar, M., & Foh, C. H. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11385-1 · Cited by 10
A Heterogeneous Federated Transfer Learning Approach with Extreme Aggregation and Speed
Berghout, T., Bentrcia, T., Ferrag, M. A., & Benbouzid, M. (2022) · Mathematics · doi:10.3390/math10193528 · Cited by 10
Zormati, M. A., Lakhlef, H., & Ouni, S. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3635995 · Cited by 9
HTTPS: Heterogeneous Transfer learning for spliT Prediction System evaluated on healthcare data
Syu, J.-H., Fojcik, M., Cupek, R., & Lin, J. C. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102617 · Cited by 10
FedZaCt: Federated Learning with Z Average and Cross-Teaching on Image Segmentation
Yang, T., Xu, J., Zhu, M., An, S., Gong, M., & Zhu, H. (2022) · Electronics · doi:10.3390/electronics11203262 · Cited by 9
Mehta, A., & Verma, R. K. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06210-w · Cited by 9
Federated Learning-Based In-Network Traffic Analysis on IoT Edge
Zang, M., Zheng, C., Koziak, T., Zilberman, N., & Dittmann, L. (2023) · IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking57963.2023.10186438 · Cited by 9
D2D-Assisted Adaptive Federated Learning in Energy-Constrained Edge Computing
Li, Z., Zhang, K., Zhang, Y., Liu, Y., & Chen, Y. (2024) · Applied Sciences · doi:10.3390/app14124989 · Cited by 8
Khan, A. A., Timilsina, L., Muriithi, G., Arsalan, A., Moghassemi, A., Papari, B., Özkan, G., Edrington, C. S., Boghrabadi, N. S., & Wang, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3613231 · Cited by 9
Blockchain based Federated Deep Learning Framework for Malware Attacks Detection in IoT Devices
Rawat, P., & Kumar, P. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10306828 · Cited by 8
On Enhancing Network Slicing Life-Cycle Through an AI-Native Orchestration Architecture
Moreira, R., Martins, J. S. B., Carvalho, T. C. M. D. B., & Silva, F. D. O. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-28451-9_11 · Cited by 8
Khan, N. A., Akhtar, M. M., Siddiqi, A. M. U., Rashid, K. A., Rajeyyagari, S., Khalid, M. N., & Ahmad, M. K. (2024) · Springer tracts in nature-inspired computing · doi:10.1007/978-981-97-7344-2_6 · Cited by 7
Federated Class-Incremental Learning via Weighted Aggregation and Distillation
Wu, F., Tan, A. Z., Feng, S., Yu, H., Deng, T., Zhao, L., & Chen, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3553901 · Cited by 7
Nanekaran, N. P., & Ukwatta, E. (2025) · Medical Physics · doi:10.1002/mp.18064 · Cited by 8
Önlü, A. Ö., Akca, B., Büyüktanır, B., Yıldız, K., & Baydoğmus, G. K. (2025) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu67174.2025.11208434 · Cited by 7
SimProx: A Similarity-Based Aggregation in Federated Learning With Client Weight Optimization
El-Niss, A., Alzu’bi, A., Abuarqoub, A., Hammoudeh, M. A. A., & Muthanna, A. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3513816 · Cited by 7
Training Effective Neural Networks on Structured Data with Federated Learning
Pustozerova, A., Rauber, A., & Mayer, R. L. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-75075-6_32 · Cited by 7
Federated learning privacy incentives: Reverse auctions and negotiations
Lyu, H., Zhang, Y., Wang, C., Long, S., & Guo, S. (2023) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12190 · Cited by 7
Attribute recognition for person re-identification using federated learning at all-in-edge
Girija, S., Baker, T., Ahmed, N., Khedr, A. M., Aghbari, Z. A., Jha, A., Соболев, К., Ahmadi‐Asl, S., & Phan, A. H. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100793 · Cited by 9
Adaptive Regularization and Resilient Estimation in Federated Learning
Uddin, M. P., Xiang, Y., Zhao, Y., Ali, M., Zhang, Y., & Gao, L. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3332703 · Cited by 6
An IoMT-based Federated Learning Survey in Smart Transportation
Karnam, G. V., & Maddikunta, P. K. R. (2023) · Recent Advances in Computer Science and Communications · doi:10.2174/0126662558286756231206062720 · Cited by 7
Ahmed, K. M., Das, B. C., Saadati, Y., & Amini, M. H. (2024) · Big and integrated artificial intelligence · doi:10.1007/978-3-031-57567-9_4 · Cited by 6
Federated-Learning Intrusion Detection System Based Blockchain Technology
Almaghthawi, A., Ghaleb, E. A. A., Akbar, N. A., Asiri, L., Alrehaili, M., & Altalidi, A. (2024) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v20i11.49949 · Cited by 8
Federated Deep Learning Models for Intrusion Detection in IoT
Ennaji, E. M., Hajla, S. E., Maleh, Y., & Mounir, S. (2024) · International Conference on Networking, Information Systems & Security (NISS) · doi:10.1145/3659677.3659837 · Cited by 6
FedCo: Self-Supervised Learning in Federated Learning with Momentum Contrast
Wei, S., Cao, G., Dai, C., Dai, S., & Guo, B. (2022) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys57074.2022.00192 · Cited by 6
Drowsiness Detection Using Federated Learning: Lessons Learnt from Dealing with Non-IID Data
Dautov, R., & Husom, E. J. (2024) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3652037.3652074 · Cited by 6
Performance Measurement of Federated Learning on Imbalanced Data
Sittijuk, P., & Tamee, K. (2021) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse53117.2021.9493819 · Cited by 6
Service-based Federated Deep Reinforcement Learning for Anomaly Detection in Fog Ecosystems
Al-Naday, M., Reed, M. J., Dobre, V., Salman Zubair, T. O. O. R., Volckaert, B., & Turck, F. D. (2023) · Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) · doi:10.1109/icin56760.2023.10073495 · Cited by 7
Experimental Standards for Deep Learning in Natural Language Processing Research
Ulmer, D., Bassignana, E., Müller-Eberstein, M., Varab, D., Zhang, M., van der Goot, R., Hardmeier, C., & Plank, B. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.196 · Cited by 9
A Framework for testing Federated Learning algorithms using an edge-like environment
Schwanck, F. M., Leipnitz, M. T., Carbonera, J. L., & Wickboldt, J. A. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107626 · Cited by 5
Gupta, H. K., Krishnan, A., Vyas, O. P., Merlino, G., Longo, F., & Puliafito, A. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107878 · Cited by 5
Distributed Anomaly Detection Using Federated Learning
Singh, P. K., Chowdhury, A., & Pal, A. (2025) · IEEE Access · doi:10.1109/access.2025.3600450 · Cited by 5
Trusted Compute Units: A Framework for Chained Verifiable Computations
Castillo, F., Heiss, J., Werner, S., & Tai, S. (2025) · IEEE International Conference on Blockchain and Cryptocurrency (ICBC) · doi:10.1109/icbc64466.2025.11114627 · Cited by 5
A Privacy Preserving Federated Learning System for IoT Devices Using Blockchain and Optimization
Han, Y. (2024) · Journal of Computer and Communications · doi:10.4236/jcc.2024.129005 · Cited by 5
Privacy-preserving deep learning model with integer quantization and secure multi-party computation
Tran, A.-T., Luong, T. D., & Pham, X. S. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06151-w · Cited by 5
Wang, D., Chen, L., Lu, X., Wang, Y., Shen, Y., & Xu, J. (2024) · IEEE Access · doi:10.1109/access.2024.3379273 · Cited by 5
Süzen, A. A., & Şimşek, M. A. (2020) · Namık Kemal Tıp Dergisi · doi:10.37696/nkmj.660762 · Cited by 5
Federated Learning Approach for Privacy-Preserving on the D2D Communication in IoT
Alam, T. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-85990-9_31 · Cited by 5
Differentially Private Auction for Federated Learning with Non-IID Data
Ren, K. (2022) · International Conference on Service Science (ICSS) · doi:10.1109/icss55994.2022.00054 · Cited by 5
FLAGS framework for comparative analysis of Federated Learning algorithms
Lodhi, A. H., Akgün, B., & Özkasap, Ö. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100638 · Cited by 5
Performance Analysis of Federated Learning in Orbital Edge Computing
Jabbarpour, M. R., Javadi, B., Leong, P. H. W., Calheiros, R. N., Boland, D., & Butler, C. (2023) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1145/3603166.3632140 · Cited by 5
Janapa, V. S. S. L. D., Sundaraneedi, N. V. S. S., Tiyyagura, P. R., Maddu, L. S. G., Sikhakolli, S. K., Elumalai, K., Kuruguntla, L., & Dodda, V. C. (2025) · Journal of Electronic Materials · doi:10.1007/s11664-025-12160-7 · Cited by 4
Fast Convergent Federated Learning via Decaying SGD Updates
Uddin, M. P., Xiang, Y., Hasan, M., Zhao, Y., Qu, Y., & Gao, L. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3618454 · Cited by 4
Comparison of federated learning frameworks for discrete event manufacturing systems
Tapia, E., López-Novoa, U., Pinilla, L. S., & de Lacalle, L. N. L. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01185-8 · Cited by 4
Hierarchical Federated Learning for Edge Intelligence through Average Consensus
Menegatti, D., Manfredi, S., Pietrabissa, A., Poli, C., & Giuseppi, A. (2023) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.10.1673 · Cited by 5
Federated Learning in Data Privacy and Security
Reddy, D. T., Nandigam, H., Indla, S. C., & Raja, S. P. (2024) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij.31647 · Cited by 4
An Extensive Overview of anamoly Detection Analysis in FL-Based IDS
Tyagi, H., & Singh, A. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.03.334 · Cited by 4
DWFL: Enhancing Federated Learning Through Dynamic Weighted Averaging
Chourasia, P., Ali, T. E., Ali, S., & Pattersn, M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-91428-7_3 · Cited by 5
On the Neural Backdoor of Federated Generative Models in Edge Computing
Wang, D., Wen, S., Jolfaei, A., Haghighi, M. S., Nepal, ., & Xiang, Y. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3425662 · Cited by 4
Federated-Learning-Assisted Failure-Cause Identification in Microwave Networks
Tandel, T., Ayoub, O., Musumeci, F., Passera, C., & Tornatore, M. (2022) · International Workshop on Resilient Networks Design and Modeling (RNDM) · doi:10.1109/rndm55901.2022.9927592 · Cited by 4
Shubyn, B., Grzesik, P., Maksymyuk, T., Kostrzewa, D., Benecki, P., Syu, J.-H., Lin, J. C., Sunderam, V. S., & Mrozek, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36030-5_39 · Cited by 4
Federated Learning - Opportunities and Application Challenges
Ilić, M., & Ivanović, M. D. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-41774-0_38 · Cited by 4
Agent Selection Framework for Federated Learning in Resource-Constrained Wireless Networks
Raftopoulou, M., da Silva, J. M. B., Litjens, R., Poor, H. V., & Mieghem, P. V. (2024) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2024.3450829 · Cited by 4
Performance Analysis of Architectural Patterns for Federated Learning Systems
Compagnucci, I., Pinciroli, R., & Trubiani, C. (2025) · IEEE International Conference on Software Architecture (ICSA) · doi:10.1109/icsa65012.2025.00036 · Cited by 4
Federated Fine-Tuning Performance on Edge Devices
Orescanin, M., Ergezer, M., Singh, G., & Baxter, M. (2021) · International Conference on Machine Learning and Applications · doi:10.1109/icmla52953.2021.00191 · Cited by 4
A Survey on Federated Learning Technology
Zheng, X., Chen, Y., Li, Z., & He, R. (2023) · International Conference on Mathematics and Artificial Intelligence (ICMAI) · doi:10.1145/3594300.3594312 · Cited by 8
Sittijuk, P., & Tamee, K. (2024) · Symmetry · doi:10.3390/sym16091181 · Cited by 6
Privacy Preserving Federated Learning Solution for Security of Industrial Cyber Physical Systems
Majidi, S. H., & Asharioun, H. (2021) · Springer eBooks · doi:10.1007/978-3-030-76613-9_11 · Cited by 4
Metadata
- Authors
- Nilsson, A., Smith, S., Ulm, G., Gustavsson, E., & Jirstrand, M.
- Affiliations
- Fraunhofer Chalmers Research Centre for Industrial Mathematics (SE)
- Venue
- Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning
- Publisher
- ACM
- Published
- 2018-12-10
- Pages
- 1-8
- OpenAlex
- W2900594532
- Cited by
- 328
- References
- 12
- Citations by year
- 2026: 31 · 2025: 52 · 2024: 80 · 2023: 68 · 2022: 51 · 2021: 24 · 2020: 15 · 2019: 7
- FWCI
- 4.68
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Funders
- VINNOVA (2016-04260)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- federated learning, FedAvg, co-ops, centralized learning, non-IID data, data heterogeneity, network heterogeneity, MNIST, distributed learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review