mlsanityData to 7 Oct 2026

Federated Learning under Heterogeneous and Correlated Client Availability

arXiv 2301.04632 ↗First posted 11 Jan 2023cs.LG cs.AI cs.DCcited by 18

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Abstract (arXiv)

The enormous amount of data produced by mobile and IoT devices has motivated the development of federated learning (FL), a framework allowing such devices (or clients) to collaboratively train machine learning models without sharing their local data. FL algorithms (like FedAvg) iteratively aggregate model updates computed by clients on their own datasets. Clients may exhibit different levels of participation, often correlated over time and with other clients. This paper presents the first convergence analysis for a FedAvg-like FL algorithm under heterogeneous and correlated client availability. Our analysis highlights how correlation adversely affects the algorithm's convergence rate and how the aggregation strategy can alleviate this effect at the cost of steering training toward a biased model. Guided by the theoretical analysis, we propose CA-Fed, a new FL algorithm that tries to balance the conflicting goals of maximizing convergence speed and minimizing model bias. To this purpose, CA-Fed dynamically adapts the weight given to each client and may ignore clients with low availability and large correlation. Our experimental results show that CA-Fed achieves higher time-average accuracy and a lower standard deviation than state-of-the-art AdaFed and F3AST, both on synthetic and real datasets.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 28 of its 49 references that are in the map. First posted on arXiv 11 Jan 2023.

Builds on

28 of 49 references.

References · 28

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2021Convex optimizationcited by 2,479
  6. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  7. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  8. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  9. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  10. 2020Adaptive Federated Optimizationcited by 1,036
  11. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  12. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
16 more
  1. 2019Towards Federated Learning at Scale: System Designcited by 826
  2. 2013An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networkscited by 711
  3. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  4. 2017Measuring Catastrophic Forgetting in Neural Networkscited by 359
  5. 2021A Field Guide to Federated Optimizationcited by 312
  6. 2019A Survey on Distributed Machine Learningcited by 187
  7. 2020Optimal Client Sampling for Federated Learningcited by 123
  8. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  9. 2018On Markov Chain Gradient Descentcited by 63
  10. 2021FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learningcited by 43
  11. 2019Semi-Cyclic Stochastic Gradient Descentcited by 40
  12. 2022Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraintscited by 30
  13. 2020Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomnesscited by 18
  14. 2020Convergence Rates of Accelerated Markov Gradient Descent with Applications in Reinforcement Learningcited by 15
  15. 2020Distributed Optimization over Block-Cyclic Datacited by 6
  16. 2020Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithmscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 6; 2026: 3.

18 papers.

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  1. 2024SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Frameworkcited by 21
  2. 2025LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Datacited by 15
  3. 2024Workflow Optimization for Parallel Split Learningcited by 8
  4. 2024Robust Decentralized Learning with Local Updates and Gradient Trackingcited by 8
  5. 2024FedStale: leveraging stale client updates in federated learningcited by 5
  6. 2023Federated Learning Under Restricted User Availabilitycited by 4
  7. 2024Federated Learning over Connected Modescited by 3
  8. 2023Controlling Federated Learning for Covertnesscited by 2
  9. 2025Green Federated Learning via Carbon-Aware Client and Time Slot Schedulingcited by 2
  10. 2024Debiasing Federated Learning with Correlated Client Participationcited by 1
  11. 2025Energy-Efficient Federated Learning for AIoT using Clustering Methodscited by 1
  12. 2024Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networkscited by 0
6 more
  1. 2025TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correctioncited by 0
  2. 2025Semi-decentralized Federated Time Series Prediction with Client Availability Budgetscited by 0
  3. 2025SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Datacited by 0
  4. 2026Mechanism Design for Federated Learning with Non-Monotonic Network Effectscited by 0
  5. 2026Robust Synchronisation for Federated Learning in The Face of Correlated Device Failurecited by 0
  6. 2026Hidden in Rounds: Predicting the Time Cost of 802.11 Contention in Federated Learningcited by 0