Summary of Noise-robust and Resource-efficient Admm-based Federated Learning, by Ehsan Lari et al.
Noise-Robust and Resource-Efficient ADMM-based Federated Learning
by Ehsan Lari, Reza Arablouei, Vinay Chakravarthi Gogineni, Stefan Werner
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Signal Processing (eess.SP)
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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 The authors propose a novel federated learning (FL) algorithm that enhances robustness against communication noise while reducing communication load. They frame weighted least-squares regression as a distributed convex optimization problem, employing random scheduling for improved communication efficiency. The algorithm iteratively solves this problem using the alternating direction method of multipliers (ADMM), and introduces key modifications to eliminate the dual variable, improving robustness against additive communication noise. Clients can continue local updates even when not selected by the server, leading to substantial performance improvements. Theoretical analysis confirms convergence in both mean and mean-square senses, even with noisy links. Numerical results validate the effectiveness of the proposed algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for machines to learn together over the internet without sharing their data. This is important because sometimes the information they share can be wrong or lost, which makes the learning process less accurate. The authors come up with a solution by framing an optimization problem and using an iterative method to solve it. They also add some tricks to make sure the machines can still learn even when they don’t get the information they need from each other. This leads to better results and faster learning. The paper shows that this new approach works well in theory and practice. |
Keywords
» Artificial intelligence » Federated learning » Optimization » Regression
