mlsanityData to 14 Oct 2026

Federated Learning with Erroneous Communication Links

arXiv 2201.12991 ↗First posted 31 Jan 2022cs.LG cs.IT math.ITcited by 5

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Abstract (arXiv)

In this paper, we consider the federated learning (FL) problem in the presence of communication errors. We model the link between the devices and the central node (CN) by a packet erasure channel, where the local parameters from devices are either erased or received correctly by CN with probability ε and 1-ε, respectively. We proved that the FL algorithm in the presence of communication errors, where the CN uses the past local update if the fresh one is not received from a device, converges to the same global parameter as that the FL algorithm converges to without any communication error. We provide several simulation results to validate our theoretical analysis. We also show that when the dataset is uniformly distributed among devices, the FL algorithm that only uses fresh updates and discards missing updates might converge faster than the FL algorithm that uses past local updates.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 31 Jan 2022.

Builds on

11 of 19 references.

References · 11

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2014Convex Optimization: Algorithms and Complexitycited by 575
  4. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  5. 2019Federated Learning over Wireless Fading Channelscited by 183
  6. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  7. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  8. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  9. 2017GIANT: Globally Improved Approximate Newton Method for Distributed Optimizationcited by 44
  10. 2019Accelerating DNN Training in Wireless Federated Edge Learning Systemscited by 34
  11. 2020Communication Efficient Federated Learning with Energy Awareness over Wireless Networkscited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 2.

5 papers.

  1. 2023SplitFed resilience to packet loss: Where to split, that is the questioncited by 2
  2. 2024Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environmentscited by 1
  3. 2024Optimizing Split Points for Error-Resilient SplitFed Learningcited by 1
  4. 2026Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learningcited by 0
  5. 2026Federated Learning with Hypergradient-based Online Update of Aggregation Weightscited by 0