# Decentralized Federated Learning Over Imperfect Communication Channels

arXiv: https://arxiv.org/abs/2405.12894 · HTML: https://mlsanity.com/paper/2405.12894/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 21 May 2024.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks](https://mlsanity.com/paper/1912.13163/) (2019)
- [Towards Flexible Device Participation in Federated Learning](https://mlsanity.com/paper/2006.06954/) (2020)
- [Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis](https://mlsanity.com/paper/2101.12704/) (2021)
- [Federated Learning: A Signal Processing Perspective](https://mlsanity.com/paper/2103.17150/) (2021)
- [Convergence of Federated Learning over a Noisy Downlink](https://mlsanity.com/paper/2008.11141/) (2020)
- [Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks](https://mlsanity.com/paper/2012.05137/) (2020)
- [SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication](https://mlsanity.com/paper/2210.14026/) (2022)
- [Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data](https://mlsanity.com/paper/2206.00737/) (2022)
- [Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems](https://mlsanity.com/paper/2007.03278/) (2020)
- [Asynchronous Decentralized Learning over Unreliable Wireless Networks](https://mlsanity.com/paper/2202.00955/) (2022)
- [Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations](https://mlsanity.com/paper/2202.13606/) (2022)
- [When Decentralized Optimization Meets Federated Learning](https://mlsanity.com/paper/2306.02570/) (2023)

## Built on by (most cited first, 5 of 5)

- [Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networks](https://mlsanity.com/paper/2409.17189/) (2024)
- [Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?](https://mlsanity.com/paper/2506.12749/) (2025)
- [Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning](https://mlsanity.com/paper/2506.14251/) (2025)
- [Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints](https://mlsanity.com/paper/2606.28342/) (2026)
- [Decentralized Federated Learning for Heterogeneous Multi-Task Semantic Communication](https://mlsanity.com/paper/2608.15256/) (2026)

## Abstract (arXiv)

This paper analyzes the impact of imperfect communication channels on decentralized federated learning (D-FL) and subsequently determines the optimal number of local aggregations per training round, adapting to the network topology and imperfect channels. We start by deriving the bias of locally aggregated D-FL models under imperfect channels from the ideal global models requiring perfect channels and aggregations. The bias reveals that excessive local aggregations can accumulate communication errors and degrade convergence. Another important aspect is that we analyze a convergence upper bound of D-FL based on the bias. By minimizing the bound, the optimal number of local aggregations is identified to balance a trade-off with accumulation of communication errors in the absence of knowledge of the channels. With this knowledge, the impact of communication errors can be alleviated, allowing the convergence upper bound to decrease throughout aggregations. Experiments validate our convergence analysis and also identify the optimal number of local aggregations on two widely considered image classification tasks. It is seen that D-FL, with an optimal number of local aggregations, can outperform its potential alternatives by over 10% in training accuracy.