Summary of Adaptive Client Selection with Personalization For Communication Efficient Federated Learning, by Allan M. De Souza et al.
Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
by Allan M. de Souza, Filipe Maciel, Joahannes B. D. da Costa, Luiz F. Bittencourt, Eduardo Cerqueira, Antonio A. F. Loureiro, Leandro A. Villas
First submitted to arxiv on: 26 Nov 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 A medium-difficulty summary of this paper on Federated Learning (FL) introduces a solution called ACSP-FL that reduces communication and computation costs for training models in FL environments. This approach employs a dynamic client selection strategy to adapt the number of devices training the model and rounds required for convergence, enabling personalized model updates to improve clients’ performance. A human activity recognition dataset use case demonstrates the benefits of ACSP-FL compared to state-of-the-art approaches. Experimental evaluations show that ACSP-FL minimizes overall communication and computation overheads while providing efficient convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning (FL) is a way for many devices to work together on machine learning projects. FL needs strong communication between the devices and a central server, but this can be slow and hard to scale. This paper introduces ACSP-FL, a new approach that makes FL faster and cheaper by letting devices pick which ones should train the model and how long it takes. This helps make personalized models for each device. The authors tested this on human activity recognition data and showed that it works better than other methods. |
Keywords
* Artificial intelligence * Activity recognition * Federated learning * Machine learning
