EntropicFL: Efficient Federated Learning via Data Entropy and Model Divergence
Author:
Affiliation:
1. Instituto de Computação, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
2. Instituto de Computação, Universidade Estadual de Campinas, Campinas, Brazil
Funder
MCTI
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3603166.3632611
Reference21 articles.
1. FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning
2. Daniel J. Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D. Lane. 2020. Flower: A Friendly Federated Learning Research Framework. CoRR abs/2007.14390 (2020). arXiv:2007.14390 https://arxiv.org/abs/2007.14390
3. Client Selection in Federated Learning: Principles, Challenges, and Opportunities
4. A systematic review of federated learning: Challenges, aggregation methods, and development tools
5. ContextFL: Context-aware Federated Learning by Estimating the Training and Reporting Phases of Mobile Clients
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