mlsanityData to 14 Oct 2026

Federated Learning Under Restricted User Availability

arXiv 2309.14176 ↗First posted 25 Sep 2023cs.LG cs.DC stat.MLcited by 4

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Abstract (arXiv)

Federated Learning (FL) is a decentralized machine learning framework that enables collaborative model training while respecting data privacy. In various applications, non-uniform availability or participation of users is unavoidable due to an adverse or stochastic environment, the latter often being uncontrollable during learning. Here, we posit a generic user selection mechanism implementing a possibly randomized, stationary selection policy, suggestively termed as a Random Access Model (RAM). We propose a new formulation of the FL problem which effectively captures and mitigates limited participation of data originating from infrequent, or restricted users, at the presence of a RAM. By employing the Conditional Value-at-Risk (CVaR) over the (unknown) RAM distribution, we extend the expected loss FL objective to a risk-aware objective, enabling the design of an efficient training algorithm that is completely oblivious to the RAM, and with essentially identical complexity as FedAvg. Our experiments on synthetic and benchmark datasets show that the proposed approach achieves significantly improved performance as compared with standard FL, under a variety of setups.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 25 Sep 2023.

Builds on

17 of 26 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2019Towards Federated Learning at Scale: System Designcited by 832
  5. 2017Federated Multi-Task Learningcited by 672
  6. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  7. 2019Fair Resource Allocation in Federated Learningcited by 394
  8. 2021A Field Guide to Federated Optimizationcited by 315
  9. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 198
  10. 2020Optimal Client Sampling for Federated Learningcited by 124
  11. 2020Towards Flexible Device Participation in Federated Learningcited by 65
  12. 2019Semi-Cyclic Stochastic Gradient Descentcited by 40
5 more
  1. 2022Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraintscited by 30
  2. 2022Proportional Fairness in Federated Learningcited by 21
  3. 2023Federated Learning under Heterogeneous and Correlated Client Availabilitycited by 18
  4. 2022FedGS: Federated Graph-based Sampling with Arbitrary Client Availabilitycited by 10
  5. 2020Noisy Linear Convergence of Stochastic Gradient Descent for CV@R Statistical Learning under Polyak-Lojasiewicz Conditionscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 31
  2. 2025Convergence of Agnostic Federated Averagingcited by 2
  3. 2025FedAVOT: Exact Distribution Alignment in Federated Learning via Masked Optimal Transportcited by 1
  4. 2026FedeRage: Provably Convergent Agnostic Federated Learning under General Client Driftcited by 0