Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory
Wei Huang, Ye Shi, Zhongyi Cai, Taiji Suzuki
2/12/2026
No confident code implementation found for this paper.
Code Implementations
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
Similar Papers
AnalogGenie-Lite: Enhancing Scalability and Precision in Circuit Topology Discovery through Lightweight Graph Modeling
96% match2026
Encoding Human Behavior in Information Design through Deep Learning
96% match2026
Towards Empowerment Gain through Causal Structure Learning in Model-Based Reinforcement Learning
92% match2026
Learning Fine-Grained Representations through Textual Token Disentanglement in Composed Video Retrieval
92% match1 repo232026
Learning Simultaneous Navigation and Construction in Grid Worlds
88% match1 repo192026
Enhancing Value Function Estimation through First-Order State-Action Dynamics in Offline Reinforcement Learning
88% match2026
Cite this paper
@article{huang2026understanding,
title = {Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory},
author = {Wei Huang and Ye Shi and Zhongyi Cai and Taiji Suzuki},
year = {2026},
url = {https://api.semanticscholar.org/CorpusID:4d965e726be4e0b79cc7bf9aaf8ac4ef84f84076},
journal = {ICLR 2024 2024}
}Add a badge to your README
Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/4d965e726be4e0b79cc7bf9aaf8ac4ef84f84076)