mlsanityData to 13 Oct 2026

On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing

arXiv 2303.10695 ↗First posted 19 Mar 2023cs.LG cs.SY eess.SYcited by 16

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Abstract (arXiv)

Decentralized learning and optimization is a central problem in control that encompasses several existing and emerging applications, such as federated learning. While there exists a vast literature on this topic and most methods centered around the celebrated average-consensus paradigm, less attention has been devoted to scenarios where the communication between the agents may be imperfect. To this end, this paper presents three different algorithms of Decentralized Federated Learning (DFL) in the presence of imperfect information sharing modeled as noisy communication channels. The first algorithm, Federated Noisy Decentralized Learning (FedNDL1), comes from the literature, where the noise is added to their parameters to simulate the scenario of the presence of noisy communication channels. This algorithm shares parameters to form a consensus with the clients based on a communication graph topology through a noisy communication channel. The proposed second algorithm (FedNDL2) is similar to the first algorithm but with added noise to the parameters, and it performs the gossip averaging before the gradient optimization. The proposed third algorithm (FedNDL3), on the other hand, shares the gradients through noisy communication channels instead of the parameters. Theoretical and experimental results demonstrate that under imperfect information sharing, the third scheme that mixes gradients is more robust in the presence of a noisy channel compared with the algorithms from the literature that mix the parameters.

Key facts. Cited by 16 papers in the map (data through 13 Oct 2026). Builds on 28 of its 42 references that are in the map. First posted on arXiv 19 Mar 2023.

Builds on

28 of 42 references.

References · 28

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,981
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,470
  4. 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 364
  5. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  6. 2019Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communicationcited by 220
  7. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 190
  8. 2019Federated Learning over Wireless Fading Channelscited by 182
  9. 2016Achieving Geometric Convergence for Distributed Optimization over Time-Varying Graphscited by 149
  10. 2017Network Topology and Communication-Computation Tradeoffs in Decentralized Optimizationcited by 122
  11. 2017Optimal algorithms for smooth and strongly convex distributed optimization in networkscited by 83
  12. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 73
16 more
  1. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  2. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  3. 2018An Exact Quantized Decentralized Gradient Descent Algorithmcited by 36
  4. 2019High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learningcited by 32
  5. 2022Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approachcited by 27
  6. 2020Fast Convergence Algorithm for Analog Federated Learningcited by 17
  7. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  8. 2020Can Primal Methods Outperform Primal-dual Methods in Decentralized Dynamic Optimization?cited by 10
  9. 2021Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphscited by 9
  10. 2020Communication-efficient Decentralized Local SGD over Undirected Networkscited by 8
  11. 2021Federated Learning over Noisy Channels: Convergence Analysis and Design Examplescited by 8
  12. 2020Decentralized Optimization On Time-Varying Directed Graphs Under Communication Constraintscited by 5
  13. 2021Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantizationcited by 5
  14. 2021Distributed Optimization over Time-varying Graphs with Imperfect Sharing of Informationcited by 5
  15. 2020Stability of Decentralized Gradient Descent in Open Multi-Agent Systemscited by 4
  16. 2022Almost Sure Convergence of Distributed Optimization with Imperfect Information Sharingcited by 2

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Citing papers per year: 2023: 5; 2024: 9; 2025: 2.

16 papers.

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  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 40
  3. 2023Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognitioncited by 19
  4. 2024Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selectioncited by 10
  5. 2024Byzantine-Robust Aggregation for Securing Decentralized Federated Learningcited by 7
  6. 2023FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communicationcited by 4
  7. 2023Graph GOSPA metric: a metric to measure the discrepancy between graphs of different sizescited by 3
  8. 2024FedAST: Federated Asynchronous Simultaneous Trainingcited by 3
  9. 2024Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networkscited by 3
  10. 2024Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walkscited by 2
  11. 2024Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGDcited by 2
  12. 2025Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learningcited by 2
4 more
  1. 2024Boosting Fairness and Robustness in Over-the-Air Federated Learningcited by 1
  2. 2024FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysiscited by 1
  3. 2024Equitable Federated Learning with Activation Clusteringcited by 0
  4. 2025Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Datacited by 0