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Summary of Federated Learning Under Partially Class-disjoint Data Via Manifold Reshaping, by Ziqing Fan et al.


Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping

by Ziqing Fan, Jiangchao Yao, Ruipeng Zhang, Lingjuan Lyu, Ya Zhang, Yanfeng Wang

First submitted to arxiv on: 29 May 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)

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GrooveSquid.com Paper Summaries

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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 addresses the limitation of statistical heterogeneity in federated learning (FL), which can severely impact its performance. The authors propose a manifold reshaping approach called FedMR to calibrate the feature space of local training, addressing the biased optimization direction induced by partially class-disjoint data (PCDD). This is achieved by adding two interplaying losses: intra-class loss for anti-collapse and inter-class loss for proper margin among categories in the feature expansion. The authors demonstrate the effectiveness of their approach through extensive experiments on various datasets.
Low GrooveSquid.com (original content) Low Difficulty Summary
In this paper, researchers are trying to make a type of machine learning called federated learning work better when different devices or “clients” contribute different pieces of information. They’re proposing a new way to make sure all the information is used correctly and that the algorithm doesn’t get stuck in a bad place. This can help with things like self-driving cars, medical diagnosis, and more.

Keywords

» Artificial intelligence  » Federated learning  » Machine learning  » Optimization