FedBoost: A Communication-Efficient Algorithm for Federated Learning

By Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh. 2020

247 citations recorded by the source corpus.

Subfields: Federated & Distributed Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Federated & Distributed Learning

1. Dacheng Tao
2. Xiao Wang
3. Bo Du
4. Jiankun Hu
5. Pong Chi Yuen
6. Lixu Wang
7. Qi Zhu
8. Yanming Zhu

Citations in the corpus

• Federated Class-Incremental Learning (2022, background): “[34] integrates unsupervised domain adaptation [5, 9, 27, 28, 57] into federated learning framework [14, 31].”
• A Comprehensive Survey of Privacy-preserving Federated Learning (2021, background): “The focuses of FL research can be roughly divided into three aspects: (1) improving the efficiency and effectiveness of FL [24, 68, 87, 100, 120, 211, 213], (2) improving the security of FL to attacks that aim to undermine the integrity of FL models and degrade the model performance [9, 13, 185, 198”
• Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, background): “Therefore, improving communication efficiency and effectiveness is the focus of many existing approaches [11, 59, 68, 211].”