Privacy-preserving and communication-efficient federated learning in Internet of Things
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Due to the continuity and gradualness of the convergence process, the historical gradient can usually be used to estimate the current gradient.
“Due to the continuity and gradualness of the convergence process [25], the historical gradient can usually be used to estimate the current gradient.”
From A Blockchain‐Based Federated Learning Method for Smart Healthcare (2021) · cites this paper as [25]
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BackgroundResultA privacy-preserving and verifiable federated learning method based on blockchain
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BackgroundA Review of Research on Secure Aggregation for Federated Learning
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“Although there have been several recent approaches to augment data privacy [51], [123], all of these strategies raise the computational complexity and weight on the federated network.”
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Privacy-Preserving federated learning: An application for big data load forecast in buildings
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BackgroundMethodResultInfluentialPSFL: Ensuring Data Privacy and Model Security for Federated Learning
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“Some researchers proposed to introduce secure multiparty computing [28], differential privacy [29], [30], obfuscation techniques [31], lightweight encryption [32], and homomorphic encryption [33], [34] into FL to enhance its security.”
BackgroundFLCP: federated learning framework with communication-efficient and privacy-preserving
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BackgroundSecuring Federated Learning: Approaches, Mechanisms and Opportunities
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A Heterogeneous Federated Transfer Learning Approach with Extreme Aggregation and Speed
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Optimized Paillier Homomorphic Encryption in Federated Learning for Speech Emotion Recognition
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“To enhance the privacy and confidentiality of clients in FL, employing homomorphic encryption methods such as Paillier homomorphic encryption (PHE) can be a viable solution for SER applications [5].”
MethodMohammadi, S., Sinaei, S., Balador, A., & Flammini, F. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7032-2_1 · Cited by 9 · see citing works
“An alternative approach to ensuring privacy and preserving accuracy in SER applications is through the use of diverse homomorphic encryption methods, one of which is Paillier homomorphic encryption (PHE) [10].”
BackgroundHow does Federated Learning Impact Decision-Making in Firms: A Systematic Literature Review
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“The foundation of federated learning emerged from privacy concerns, introducing federated learning specifically to enable collaborative learning without sharing raw data [139], [140].”
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“Experiments on the datasets shows how method in [17] outperforms conventional privacy preserving techniques such as in [18[ with an acceptable communication overhead.”
BackgroundMethodInfluentialA Dropout-Tolerated Privacy-Preserving Method for Decentralized Crowdsourced Federated Learning
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“Therefore, these schemes [20], [21] can only provide complete privacy protection against external attackers, while the internal attackers can obtain private information from other workers’ ciphertexts of the gradients by decrypting them with the shared private key.”
BackgroundMethodInfluentialA Privacy-Enhanced Method for Privacy-Preserving and Verifiable Federated Learning
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Multi-Robot Privacy-Preserving Algorithms Based on Federated Learning: A Review
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Privacy preservation using optimized Federated Learning: A critical survey
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Robust and privacy-preserving federated learning scheme based on ciphertext-selected users
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Privacy-utility trades in crowdsourced signal map obfuscation
Zhang, J., Clark, L., Clark, M., Psounis, K., & Kairouz, P. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109187 · Cited by 5 · see citing works
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“The updated global model is subsequently sent back to the participating devices, commencing the next round of local training [26,27].”
A systematic review on privacy preservation in federated learning
Sum, A. S. I., Pritee, Z. T., Saha, A. K., Mahmud, M. A., & Nur, K. M. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01229-x · Cited by 6 · see citing works
RLVP-FL: Robust and lightweight verifiable privacy-preserving federated learning scheme
Zhou, P., Xie, Y., Peng, C., Kang, Y., He, D., & Mu, T. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111747 · Cited by 3 · see citing works
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Inferential Separation for Privacy: Irrelevant Statistics and Quantization
Feng, C., & Venkitasubramaniam, P. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3181859 · Cited by 3 · see citing works
A Secure and Efficient Federated Learning Scheme Based on Homomorphic Encryption and Secret Sharing
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“Fang et al. [15] combine with secret sharing [16] to split HE private key into multiple subkeys.”
BackgroundAEPPFL: Accurate and Efficient Privacy Protection Federal Learning in Industrial IoT
Li, G., Lei, H., Liu, F., Li, L., & Jin, H. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3562064 · Cited by 3 · see citing works
Tsouvalas, V., Mohammadi, S., Balador, A., Özçelebi, T., Flammini, F., & Meratnia, N. (2025) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2025.102021 · Cited by 3 · see citing works
LAS: Lightweight Aggregate Signcryption for federated learning with blockchain in IoT
Chen, Y., Tian, L., Wang, X., Lin, F., Jia, R., Zheng, Z., & Li, M. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111480 · Cited by 3 · see citing works
An LWE-Based Multi-Key Privacy-Preserving Distributed Deep Learning
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“…version) in [7], combination of Binary Elliptic Curve Cryptography (ECC) and additive homomorphism in [8], a combination of secret sharing and Elgamal homomorphic encryption in [9], homorphic re-encryption in [10] and double decryption combined with Fully Homomorphic Encryption (FHE) in [11] [12].”
BackgroundResultZhang, J., He, Y., Deng, X., Yu, X., Liu, S., & Wang, E. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3658772 · Cited by 2 · see citing works
Li, J., Zhou, Z., Tian, Y., Chen, C., Luo, H., & He, L. (2026) · Cybersecurity · doi:10.1186/s42400-026-00575-6 · Cited by 2 · see citing works
Sinaei, S., Mohammadi, M., Shrestha, R., Alibeigi, M., & Eklund, D. (2024) · 2024 27th Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd64264.2024.00063 · Cited by 2 · see citing works
“An alternative approach to preserving privacy without compromising accuracy is using diverse homomorphic encryption methods, such as Paillier homomorphic encryption (PHE) [7].”
MethodSoares, K., Shinde, A. A., & Patil, M. V. (2024) · International Journal of Computer Networks And Applications · doi:10.22247/ijcna/2025/11 · Cited by 2 · see citing works
“The work in [27] and [28] employs a homomorphic encryption scheme to preserve the privacy of the FL model.”
MPC-enabled privacy-preserving machine learning
Liu, Z. (2023) · doi:10.32657/10356/167272 · Cited by 2 · see citing works
Federated Learning Implementation with Privacy Leakage Prevention for Hand-Written Digit Recognition
Priyadarsini, N. I., & Raja, G. (2024) · International journal of electrical and computer engineering systems · doi:10.32985/ijeces.15.5.4 · Cited by 2 · see citing works
“Fang et al. [28] introduced PCFL, a privacy-preserving, communication-efficient federated learning approach for IoT.”
Digital Twin with Delay-Optimal Relay-Assisted Federated Learning
Tang, Y., Zhang, N., Wang, K., & Karagiannidis, G. K. (2024) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc62609.2024.10941887 · Cited by 1 · see citing works
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“Fang et al. [20] proposed a communication-efficient solution for IoT-based FL that integrates secret sharing and lightweight HE.”
A verifiable and efficient chained federated learning scheme for privacy protection
Wang, X., Liu, Z., Dai, M., Gong, J., & Ni, W. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111838 · Cited by 1 · see citing works
Lightweight verifiable privacy preserving federated learning
Zhang, L., Tang, B., & Xu, J. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104335 · Cited by 1 · see citing works
A Robust Privacy-Preserving Federated Learning Method Against Poisoning Attacks on Non-IID Data
Fang, C., Chang, D., Li, Y., Yang, F., Liu, Y., & Ma, B. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3658384 · Cited by 1 · see citing works
Self-citationApplication of Privacy Computing to Smart Grid Data Sharing Platform
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A method for improving the robustness of federal learning systems based on input transformation
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- Authors
- Fang, C., Guo, Y., Hu, Y., Ma, B., Feng, L., & Yin, A.
- Affiliations
- PLA Information Engineering University (CN)
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 103
- Pages
- 102199
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W3125786278
- Cited by
- 93
- References
- 30
- Citations by year
- 2026: 10 · 2025: 27 · 2024: 23 · 2023: 16 · 2022: 13 · 2021: 4
- FWCI
- 4.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers & Security (publishedVersion)
- Funders
- PLA Information Engineering University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- federated learning, Internet of Things, privacy preservation, communication efficiency, gradient sparsification, gradient quantization, secret sharing, homomorphic encryption, collusion resistance, 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.
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