# On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing

arXiv: https://arxiv.org/abs/2303.10695 · HTML: https://mlsanity.com/paper/2303.10695/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 42 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 28 of 28)

- [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)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication](https://mlsanity.com/paper/1902.00340/) (2019)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [Achieving Geometric Convergence for Distributed Optimization over Time-Varying Graphs](https://mlsanity.com/paper/1607.03218/) (2016)
- [Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization](https://mlsanity.com/paper/1709.08765/) (2017)
- [Optimal algorithms for smooth and strongly convex distributed optimization in networks](https://mlsanity.com/paper/1702.08704/) (2017)
- [Over-the-Air Federated Learning from Heterogeneous Data](https://mlsanity.com/paper/2009.12787/) (2020)
- [Design and Analysis of Uplink and Downlink Communications for Federated Learning](https://mlsanity.com/paper/2012.04057/) (2020)
- [Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems](https://mlsanity.com/paper/2101.03367/) (2021)
- [An Exact Quantized Decentralized Gradient Descent Algorithm](https://mlsanity.com/paper/1806.11536/) (2018)
- [High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning](https://mlsanity.com/paper/1910.03865/) (2019)
- [Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach](https://mlsanity.com/paper/2209.00778/) (2022)
- [Fast Convergence Algorithm for Analog Federated Learning](https://mlsanity.com/paper/2011.06658/) (2020)
- [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021)
- [Can Primal Methods Outperform Primal-dual Methods in Decentralized Dynamic Optimization?](https://mlsanity.com/paper/2003.00816/) (2020)
- [Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs](https://mlsanity.com/paper/2101.09583/) (2021)
- [Communication-efficient Decentralized Local SGD over Undirected Networks](https://mlsanity.com/paper/2011.03255/) (2020)
- [Federated Learning over Noisy Channels: Convergence Analysis and Design Examples](https://mlsanity.com/paper/2101.02198/) (2021)
- [Decentralized Optimization On Time-Varying Directed Graphs Under Communication Constraints](https://mlsanity.com/paper/2005.13189/) (2020)
- [Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantization](https://mlsanity.com/paper/2105.14089/) (2021)
- [Distributed Optimization over Time-varying Graphs with Imperfect Sharing of Information](https://mlsanity.com/paper/2106.08469/) (2021)
- [Stability of Decentralized Gradient Descent in Open Multi-Agent Systems](https://mlsanity.com/paper/2009.05445/) (2020)
- [Almost Sure Convergence of Distributed Optimization with Imperfect Information Sharing](https://mlsanity.com/paper/2210.05897/) (2022)

## Built on by (most cited first, 16 of 16)

- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition](https://mlsanity.com/paper/2304.07421/) (2023)
- [Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection](https://mlsanity.com/paper/2401.16685/) (2024)
- [Byzantine-Robust Aggregation for Securing Decentralized Federated Learning](https://mlsanity.com/paper/2409.17754/) (2024)
- [FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication](https://mlsanity.com/paper/2310.07048/) (2023)
- [Graph GOSPA metric: a metric to measure the discrepancy between graphs of different sizes](https://mlsanity.com/paper/2311.07596/) (2023)
- [FedAST: Federated Asynchronous Simultaneous Training](https://mlsanity.com/paper/2406.00302/) (2024)
- [Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networks](https://mlsanity.com/paper/2409.17189/) (2024)
- [Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks](https://mlsanity.com/paper/2401.09665/) (2024)
- [FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis](https://mlsanity.com/paper/2403.13247/) (2024)
- [Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD](https://mlsanity.com/paper/2409.17499/) (2024)
- [Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning](https://mlsanity.com/paper/2501.04817/) (2025)
- [Boosting Fairness and Robustness in Over-the-Air Federated Learning](https://mlsanity.com/paper/2403.04431/) (2024)
- [Equitable Federated Learning with Activation Clustering](https://mlsanity.com/paper/2410.19207/) (2024)
- [Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data](https://mlsanity.com/paper/2505.20485/) (2025)

## 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.