Mercury: Efficient On-Device Distributed DNN Training via Stochastic Importance Sampling

Deep Residual Learning for Image Recognition

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Attention Is All You Need

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Data Mining: Practical Machine Learning Tools and Techniques

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Learning Multiple Layers of Features from Tiny Images

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Communication-Efficient Learning of Deep Networks from Decentralized Data

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Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

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AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification

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Deep Neural Networks for Acoustic Modeling in Speech Recognition

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QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

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Scaling Distributed Machine Learning with the Parameter Server

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TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep LearningPreprint

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Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU ClustersPreprint

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Petuum: A New Platform for Distributed Machine Learning on Big Data

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GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server

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NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision

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Stochastic Optimization with Importance Sampling for Regularized Loss Minimization

Zhao, P., & Zhang, T. (2015) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 197 · see citing works

Gradient Sparsification for Communication-Efficient Distributed Optimization

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Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net TrainingPreprint

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Distream

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Convergence analysis of distributed stochastic gradient descent with shuffling

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Ako

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Deep Learning in the Era of Edge Computing: Challenges and Opportunities

Zhang, M., Zhang, F., Lane, N. D., Shu, Y., Zeng, X., Fang, B., Shen, Y., & Xu, H. F. (2020) · Wiley eBooks · doi:10.1002/9781119551713.ch3 · Cited by 57 · see citing works

Biased Importance Sampling for Deep Neural Network TrainingPreprint

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SCYLLA: QoE-aware Continuous Mobile Vision with FPGA-based Dynamic Deep Neural Network Reconfiguration

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Federated learning for the Internet of Things: Applications, challenges, and opportunities

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Client Selection in Federated Learning: Principles, Challenges, and Opportunities

Fu, L., Zhang, H., Gao, G., Zhang, M., & Liu, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3299573 · Cited by 310 · see citing works

PyramidFL

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Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

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A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges

Zhang, Y., Zeng, D., Luo, J., Fu, X., Chen, G., Xu, Z., & King, I. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3678181 · Cited by 90 · see citing works

Artificial Intelligence of Things: A Survey

Siam, M. S. I., Ahn, H., Liu, L., Alam, S., Shen, H., Cao, Z., Shroff, N. B., Krishnamachari, B., Srivastava, M. B., & Zhang, M. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3690639 · Cited by 90 · see citing works

To Store or Not? Online Data Selection for Federated Learning with Limited Storage

Gong, C., Zheng, Z.-Z., Wu, F., Shao, Y.-F., Li, B.-S., & Chen, G.-H. (2022) · The Web Conference · doi:10.1145/3543507.3583426 · Cited by 33 · see citing works

Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization

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Participant and Sample Selection for Efficient Online Federated Learning in UAV Swarms

Wu, F., Qu, Y., Wu, T., Dong, C., Guo, K., Wu, Q., & Guo, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3363181 · Cited by 24 · see citing works

DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning With Static and Streaming Datasets

Liu, W., Zhang, X., Duan, J., Joe‐Wong, C., Zhou, Z., & Chen, X. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3337016 · Cited by 24 · see citing works

Dual-Detector Reoptimization for Federated Weakly Supervised Video Anomaly Detection via Adaptive Dynamic Recursive Mapping

Su, Y. R., Li, J., An, S., Xu, H., & Peng, W. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3574406 · Cited by 21 · see citing works

Client Selection for Wireless Federated Learning With Data and Latency Heterogeneity

Chen, X., Zhou, X., Zhang, H., Sun, M., & Poor, H. V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3425757 · Cited by 20 · see citing works

InArt: In-Network Aggregation with Route Selection for Accelerating Distributed Training

Liu, J., Zhai, Y., Zhao, G., Xu, H., Fang, J., Zeng, Z., & Zhu, Y. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645394 · Cited by 18 · see citing works

Federated Learning: Challenges, SoTA, Performance Improvements and Application Domains

Schoinas, Ι., Triantafyllou, A., Ioannidis, D., Tzovaras, D. K., Drosou, A., Votis, K., Λάγκας, Θ., Argyriou, V., & Sarigiannidis, P. G. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3458088 · Cited by 10 · see citing works

ODE: An Online Data Selection Framework for Federated Learning With Limited Storage

Gong, C., Zheng, Z., Shao, Y., Li, B., Wu, F., & Chen, G. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3365534 · Cited by 9 · see citing works

Efficient one-shot Neural Architecture Search with progressive choice freezing evolutionary search

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Online Stream Sampling for Low-Memory On-Device Edge Training for WiFi Sensing

Hernandez, S. M., & Bulut, E. (2022) · ACM Workshop on Wireless Security and Machine Learning (WiseML) · doi:10.1145/3522783.3529521 · Cited by 8 · see citing works

CrowdLearning: A Decentralized Distributed Training Framework Based on Collectives of Trusted AIoT Devices

Wang, Z., Liu, S., Guo, B., Yu, Z., & Zhang, D. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3427636 · Cited by 6 · see citing works

Joint Data Retransmission and Client Selection Optimization for Error-Tolerant Federated Learning in AAV Networks

Dong, C., Wu, F., Qu, Y., Akerkar, R. A., Wu, T., Zhou, F., Wu, Q., & Guo, S. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3520557 · Cited by 6 · see citing works

Patching in Order: Efficient On-Device Model Fine-Tuning for Multi-DNN Vision Applications

Cao, Z., Cheng, Y., Zhou, Z., Lu, A., Hu, Y., Liu, J., Zhang, M., & Li, Z. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3443057 · Cited by 5 · see citing works

FLightNER: A Federated Learning Approach to Lightweight Named-Entity Recognition

Abadeer, M., Shi, W., & Corriveau, J. (2022) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom56396.2022.00098 · Cited by 4 · see citing works

Joint Device and Training Scheduling for Wireless Federated Learning

Chen, X., Zhou, X., Zhang, H., Sun, M., & Zhao, T. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3564461 · Cited by 3 · see citing works

Improving local training in federated learning via temperature scaling

Lee, K., Zhang, P., Kim, S., & Ko, J. G. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104283 · Cited by 3 · see citing works

GreedW: A Flexible and Efficient Decentralized Framework for Distributed Machine Learning

Wang, T., Jiang, X., Li, Q., & Cai, H. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3343110 · Cited by 3 · see citing works

Accelerating On-Device DNN Training Workloads via Runtime Convergence Monitor

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Trustworthy Distributed Deep Neural Network Training in an Edge Device Network

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PieBridge: Fast and Parameter-Efficient On-Device Training via Proxy Networks

Yin, W., Xu, D., Huang, G., Zhang, Y., Wei, S., Xu, M., & Liu, X. (2024) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3666025.3699327 · Cited by 1 · see citing works

HeaPS: Heterogeneity-aware participant selection for efficient federated learning

Yang, D., Hu, B., Gao, Y., Jin, A.-L., Liu, A., Yeung, K. L., & You, Y. (2025) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2025.105168 · Cited by 1 · see citing works

GeoFL: A Framework for Efficient Geo-Distributed Cross-Device Federated Learning

Gan, M., 李澜鹏, Alam, S., Liu, L., Liu, L., Zhang, M., Zeng, H., & Cao, Z. (2026) · IEEE Transactions on Networking · doi:10.1109/ton.2026.3668183 · Cited by 1 · see citing works

EEOKD: Energy-Efficient Online Knowledge Distillation for Mobile Video Inference

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FedCaSe: Enhancing Federated Learning with Heterogeneity-aware Caching and Scheduling

Khan, R. I. S., Paul, A. K., Cheng, Y., Jian, X., & Butt, A. R. (2024) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3698038.3698559 · Cited by 1 · see citing works

Online Federated Learning on Distributed Unknown Data Using UAVs

Zhang, X., Xu, H., Yin, X., Xiao, M., Wu, J., & Zhou, J. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00009 · Cited by 1 · see citing works

Data-Quality-based Aggregation Methods in Federated Learning: a Comprehensive Study

Boumediri, T., Djamaa, B., & Boulahia, S. Y. (2025) · International Conference on Pattern Analysis and Intelligent Systems (PAIS) · doi:10.1109/pais66004.2025.11126046 · Cited by 1 · see citing works

FLightNER: A Federated Learning Approach to Lightweight Named-Entity Recognition

Abadeer, M. (2022) · doi:10.22215/etd/2022-15201

ACache: An Importance-Based Cache with Augmentation Cutting for Accelerating DNN Training

Zhao, L., Zheng, X., & Wang, F. (2025) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd64889.2025.11033388

Data Divergence-Aware Client Selection via Knowledge Graph for Federated LLM Fine-Tuning

Zhang, B., Wang, D., Zhu, Y., & Han, Z. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3608172

Federated Learning in the Healthcare Industry

Mondal, R. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-99270-4_2

CrowdMesh: A Dynamic Model Parallel Training System on Mobile Devices

Wang, Z., Guo, B., Liu, S., Yu, Z., & Zhang, D. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3623109

It Takes Two: Accelerating Accurate Federated Learning through Pipelined Intra-Batch Data Sampling and Training

Nu, C., Zhang, Z., Li, Y., & Zhao, Y. (2025) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3754598.3754659

Energy-efficient online knowledge distillation for mobile video inference

Guo, G., Zhang, J., & Liu, B. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111962

ENTS: Experiences in Co-Designed Environmental Sensing

Madden, J., Taylor, S., Jaliff, L., Wu, A. X., Levy, A., Lin, J., Falah, A., Potyondy, T., Hester, J., Wells, G., et al. (2026) · doi:10.1145/3774906.3802780

RACER: A Caching Layer based Optimization for Accelerating Reinforcement Learning Workloads

Gogineni, K., Mei, Y., Gogineni, K., Wei, P., Lan, T., & Venkataramani, G. (2026) · IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) · doi:10.1109/ispass69572.2026.00051

PINCH: Predictive Importance-Sampling-Informed Cache for I/O-Bound DNN Training

Liu, Y., & Yin, S. (2026) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3832810.3832851

Efficient Hardware and Software Design for On-device Learning

Wu, Y., Tang, Y., Zeng, D., Zhang, X., Zhou, P., Shi, Y., & Hu, J. (2023) · Springer eBooks · doi:10.1007/978-3-031-39932-9_15

EdgeMesh : A hybrid distributed training mechanism for heterogeneous edge devices

Xu, F., Feng, J., Zhang, Z., Ning, L., & Jia, Y. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4706 · Cited by 2 · see citing works

TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce EdgePreprint

Kwon, Y. D., Li, R., Venieris, S. I., Chauhan, J., Lane, N. D., & Cecilia Mascolo, C. (2023) · arXiv (Cornell University) · doi:10.17863/cam.109470 · Cited by 11 · see citing works

On-device Training: A First Overview on Existing Systems

Zhu, S., Voigt, T., Rahimian, F., & Ko, J. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3696003 · Cited by 47 · see citing works

Distributed deep neural network training on edge devices

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