Summary of Towards Client Driven Federated Learning, by Songze Li et al.
Towards Client Driven Federated Learning
by Songze Li, Chenqing Zhu
First submitted to arxiv on: 24 May 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 presents Client-Driven Federated Learning (CDFL), a novel framework that decentralizes the conventional federated learning process by putting clients at the driving role. In CDFL, each client independently updates its model asynchronously by uploading locally trained models to the server and receiving customized models tailored to their local tasks. The server maintains a repository of cluster models, refining them iteratively using received client models. This framework accommodates complex dynamics in clients’ data distributions, characterized by time-varying mixtures of cluster distributions, enabling rapid adaptation to new tasks with superior performance. Unlike traditional clustered FL protocols that send multiple cluster models to clients for distribution estimation, CDFL offloads the estimation task to the server and only sends a single model to a client, improving estimation accuracy. The paper provides theoretical analysis of CDFL’s convergence and demonstrates substantial advantages in model performance and computation efficiency over baselines through extensive experiments across various datasets and system settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way for computers to learn together called Client-Driven Federated Learning (CDFL). Normally, when computers learn from each other, one central computer decides what updates the others should get. But this approach can be slow and inefficient. CDFL gives control to the individual computers, letting them update their own models independently. This makes it easier for computers to adapt to changing situations and work together more effectively. The paper shows that CDFL outperforms traditional methods in many cases, making it a promising way to improve how computers learn from each other. |
Keywords
» Artificial intelligence » Federated learning
