mlsanityData to 13 Oct 2026

FedNC: A Secure and Efficient Federated Learning Method with Network Coding

arXiv 2305.03292 ↗First posted 5 May 2023cs.LG cs.CR cs.ITcited by 2

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Abstract (arXiv)

Federated Learning (FL) is a promising distributed learning mechanism which still faces two major challenges, namely privacy breaches and system efficiency. In this work, we reconceptualize the FL system from the perspective of network information theory, and formulate an original FL communication framework, FedNC, which is inspired by Network Coding (NC). The main idea of FedNC is mixing the information of the local models by making random linear combinations of the original parameters, before uploading for further aggregation. Due to the benefits of the coding scheme, both theoretical and experimental analysis indicate that FedNC improves the performance of traditional FL in several important ways, including security, efficiency, and robustness. To the best of our knowledge, this is the first framework where NC is introduced in FL. As FL continues to evolve within practical network frameworks, more variants can be further designed based on FedNC.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

13 of 23 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  2. 2019Advances and Open Problems in Federated Learningcited by 3,040
  3. 2016Deep Learning with Differential Privacycited by 2,718
  4. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 456
  5. 2018cpSGD: Communication-efficient and differentially-private distributed SGDcited by 158
  6. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 156
  7. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  8. 2020Coded Computing for Low-Latency Federated Learning over Wireless Edge Networkscited by 32
  9. 2022Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learningcited by 28
  10. 2022Stochastic Coded Federated Learning with Convergence and Privacy Guaranteescited by 10
  11. 2020Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computingcited by 9
  12. 2023FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learningcited by 7
1 more
  1. 2021How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learningcited by 6

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026EdgeFLow: Serverless Federated Learning via Sequential Model Migration in Edge Networkscited by 1
  2. 2026FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inferencecited by 0