Summary of Fedlog: Personalized Federated Classification with Less Communication and More Flexibility, by Haolin Yu et al.
FedLog: Personalized Federated Classification with Less Communication and More Flexibility
by Haolin Yu, Guojun Zhang, Pascal Poupart
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (stat.ML)
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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 Federated representation learning (FRL) aims to develop personalized models for diverse data sources while minimizing communication overhead. Existing FRL algorithms struggle with excessive parameter sharing, resulting in slow aggregation and significant message sizes. To address this issue, we propose a novel approach that shares compact data summaries instead of raw model parameters. These summaries encode minimal sufficient statistics of an exponential family, allowing for efficient global aggregation using Bayesian inference. We further enhance our method by integrating differential privacy to ensure formal guarantees. Experimental results show promising learning accuracy and reduced communication overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to work with different pieces of data that are not shared between devices. When devices need to learn from each other, sharing too much information can be slow and take up a lot of space. To solve this problem, the authors suggest using shorter summaries of the data instead of sending all the data at once. These summaries give enough information for devices to work together while keeping communication fast and efficient. The authors also add an extra layer of protection called differential privacy to ensure that each device’s data remains private. The results show that their method works well, allowing devices to learn from each other quickly and securely. |
Keywords
* Artificial intelligence * Bayesian inference * Representation learning
