Summary of Communication-efficient Federated Low-rank Update Algorithm and Its Connection to Implicit Regularization, by Haemin Park et al.
Communication-Efficient Federated Low-Rank Update Algorithm and its Connection to Implicit Regularization
by Haemin Park, Diego Klabjan
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 addresses the challenges faced by Federated Learning (FL) in terms of communication efficiency and heterogeneity. The authors explore the potential of using low-rank updates, which they believe can provide an implicit regularization effect. They propose FedLoRU, a general framework for federated learning that enforces low-rank client-side updates and accumulates these updates to form a higher-rank model. Experimental results show that FedLoRU performs comparably to full-rank algorithms and exhibits robustness to heterogeneous environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to make Federated Learning (FL) work better. FL is a way for many devices to learn together without sharing their data. The problem is that it can be slow and difficult when the devices are different or there are lots of them. The authors found out that if they only use certain parts of the information from each device, it can make things go faster and work better. They came up with a new way to do this called FedLoRU. It works by taking small pieces of information from each device and combining them into something bigger and more useful. This helps FL learn better even when devices are different or there are lots of them. |
Keywords
» Artificial intelligence » Federated learning » Regularization
