mlsanityData to 7 Oct 2026

Federated Learning: Issues in Medical Application

arXiv 2109.00202 ↗First posted 1 Sep 2021cs.LG cs.AIcited by 4

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Abstract (arXiv)

Since the federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in the field of medicine. In fact, the idea of machine learning in AI without collecting data from local clients is very attractive because data remain in local sites. However, federated learning techniques still have various open issues due to its own characteristics such as non identical distribution, client participation management, and vulnerable environments. In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The framework will be open to public after development completes.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 48 of its 65 references that are in the map. First posted on arXiv 1 Sep 2021.

Builds on

48 of 65 references.

References · 48

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36 more
  1. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 330
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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1.

4 papers.

  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2022Privacy of federated QR decomposition using additive secure multiparty computationcited by 1
  3. 2023Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imagingcited by 1
  4. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0