mlsanityData to 7 Oct 2026

A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning

arXiv 2201.02932 ↗First posted 9 Jan 2022cs.LG cs.AIcited by 28

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) is a training technique that enables client devices to jointly learn a shared model by aggregating locally-computed models without exposing their raw data. While most of the existing work focuses on improving the FL model accuracy, in this paper, we focus on the improving the training efficiency, which is often a hurdle for adopting FL in real-world applications. Specifically, we design an efficient FL framework which jointly optimizes model accuracy, processing latency and communication efficiency, all of which are primary design considerations for real implementation of FL. Inspired by the recent success of Multi-Agent Reinforcement Learning (MARL) in solving complex control problems, we present textitFedMarl, an MARL-based FL framework which performs efficient run-time client selection. Experiments show that FedMarl can significantly improve model accuracy with much lower processing latency and communication cost.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 28 of its 38 references that are in the map. First posted on arXiv 9 Jan 2022.

Builds on

28 of 38 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2018On the Spectral Bias of Neural Networkscited by 1,321
  7. 2018Federated Learning with Non-IID Datacited by 1,099
  8. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  9. 2019Towards Federated Learning at Scale: System Designcited by 826
  10. 2017Federated Multi-Task Learningcited by 669
  11. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 490
  12. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
16 more
  1. 2019Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networkscited by 360
  2. 2020Adaptive Personalized Federated Learningcited by 353
  3. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  4. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  5. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  6. 2018TarMAC: Targeted Multi-Agent Communicationcited by 201
  7. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  8. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 192
  9. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 178
  10. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 170
  11. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  12. 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 76
  13. 2020Mitigating Bias in Federated Learningcited by 56
  14. 2019Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Controlcited by 46
  15. 2020Succinct and Robust Multi-Agent Communication With Temporal Message Controlcited by 29
  16. 2020Coded Federated Learningcited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 7; 2024: 7; 2025: 9; 2026: 2.

28 papers.

Sort:

By area:

  1. 2023Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reductioncited by 24
  2. 2022SphereFed: Hyperspherical Federated Learningcited by 19
  3. 2023Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysiscited by 11
  4. 2023FedZero: Leveraging Renewable Excess Energy in Federated Learningcited by 11
  5. 2022When Do Curricula Work in Federated Learning?cited by 9
  6. 2024FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimizationcited by 9
  7. 2024DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Modelcited by 8
  8. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  9. 2024Unlocking Visual Secrets: Inverting Features with Diffusion Priors for Image Reconstructioncited by 5
  10. 2025DSD: A Distributed Speculative Decoding Solution for Edge-Cloud Agile Large Model Servingcited by 5
  11. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
  12. 2024On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representationscited by 4
16 more
  1. 2025Fairness in Federated Learning: Trends, Challenges, and Opportunitiescited by 3
  2. 2023FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learningcited by 2
  3. 2024Ranking-based Client Selection with Imitation Learning for Efficient Federated Learningcited by 2
  4. 2024Learnable Sparse Customization in Heterogeneous Edge Computingcited by 2
  5. 2025Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learningcited by 2
  6. 2023FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learningcited by 1
  7. 2025Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Servicescited by 1
  8. 2022FedHeN: Federated Learning in Heterogeneous Networkscited by 0
  9. 2023Near-optimal Differentially Private Client Selection in Federated Settingscited by 0
  10. 2025Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Samplingcited by 0
  11. 2025Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learningcited by 0
  12. 2025Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learningcited by 0
  13. 2025Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitionscited by 0
  14. 2025BIPPO: Budget-Aware Independent PPO for Energy-Efficient Federated Learning Servicescited by 0
  15. 2026Federated Learning at the Forefront of Fairness: A Multifaceted Perspectivecited by 0
  16. 2026Similarity-Aware Personalized Federated Learning in Heterogeneous Environmentscited by 0