mlsanityData to 7 Oct 2026

FedCore: Straggler-Free Federated Learning with Distributed Coresets

arXiv 2402.00219 ↗First posted 31 Jan 2024cs.DC cs.AI cs.LGcited by 2

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Abstract (arXiv)

Federated learning (FL) is a machine learning paradigm that allows multiple clients to collaboratively train a shared model while keeping their data on-premise. However, the straggler issue, due to slow clients, often hinders the efficiency and scalability of FL. This paper presents FedCore, an algorithm that innovatively tackles the straggler problem via the decentralized selection of coresets, representative subsets of a dataset. Contrary to existing centralized coreset methods, FedCore creates coresets directly on each client in a distributed manner, ensuring privacy preservation in FL. FedCore translates the coreset optimization problem into a more tractable k-medoids clustering problem and operates distributedly on each client. Theoretical analysis confirms FedCore's convergence, and practical evaluations demonstrate an 8x reduction in FL training time, without compromising model accuracy. Our extensive evaluations also show that FedCore generalizes well to existing FL frameworks.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 39 of its 56 references that are in the map. First posted on arXiv 31 Jan 2024.

Builds on

39 of 56 references.

References · 39

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27 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026SCOPE: Semantic Coreset with Orthogonal Projection Embeddings for Federated learningcited by 0
  2. 2026EMS Coreset: An Efficient Expectation-Maximization Algorithm for Sinkhorn Coresetcited by 0