Summary of Fednmut — Federated Noisy Model Update Tracking Convergence Analysis, by Vishnu Pandi Chellapandi et al.
FedNMUT – Federated Noisy Model Update Tracking Convergence Analysis
by Vishnu Pandi Chellapandi, Antesh Upadhyay, Abolfazl Hashemi, Stanislaw H. Żak
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel Federated Learning (FL) algorithm, Decentralized Noisy Model Update Tracking (FedNMUT), designed to operate efficiently in noisy communication channels. The algorithm uses gradient tracking to minimize data heterogeneity and reduce communication overhead. By incorporating noise into its parameters, FedNMUT enables consensus among clients through a communication graph topology, increasing the resilience of decentralized learning systems against noisy communications. Theoretical results demonstrate that the algorithm achieves an -stationary solution at a rate of (), where T is the total number of communication rounds. Empirical validation shows that FedNMUT outperforms existing state-of-the-art methods and conventional parameter-mixing approaches in dealing with imperfect information sharing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to learn together even when they don’t get all the information correctly. It’s like trying to have a conversation with friends, but sometimes you mishear what they say. The new method, called FedNMUT, helps computers agree on what they’ve learned despite this noise. This is important because it makes learning more accurate and efficient in situations where not all the information can be shared. The researchers showed that their method works better than other ways of doing things, and they proved that it can even handle very noisy communication channels. |
Keywords
* Artificial intelligence * Federated learning * Tracking
