An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee
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Abstract (arXiv)
The issue of potential privacy leakage during centralized AI's model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, termed Federated Learning (FL), has emerged as a new paradigm to cope with the privacy issue by allowing clients to perform model training locally, without the necessity to upload their personal sensitive data. In FL, the number of clients could be sufficiently large, but the bandwidth available for model distribution and re-upload is quite limited, making it sensible to only involve part of the volunteers to participate in the training process. The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness. In this paper, we will model the fairness guaranteed client selection as a Lyapunov optimization problem and then a C2MAB-based method is proposed for estimation of the model exchange time between each client and the server, based on which we design a fairness guaranteed algorithm termed RBCS-F for problem-solving. The regret of RBCS-F is strictly bounded by a finite constant, justifying its theoretical feasibility. Barring the theoretical results, more empirical data can be derived from our real training experiments on public datasets.
Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Nov 2020.
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Citing papers per year: 2021: 7; 2022: 8; 2023: 19; 2024: 12; 2025: 12; 2026: 3.
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- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
- 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
- 2021Towards Fairness-Aware Federated Learningcited by 57
- 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
- 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
- 2022Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reductioncited by 32
- 2022Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimizationcited by 28
- 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 24
- 2022Fairness in Federated Learning via Core-Stabilitycited by 22
- 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
- 2023Fairness-Aware Client Selection for Federated Learningcited by 15
- 2023Fusion of Global and Local Knowledge for Personalized Federated Learningcited by 14
38 more
- 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
- 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Directioncited by 12
- 2022A Survey on Participant Selection for Federated Learning in Mobile Networkscited by 11
- 2022FedGS: Federated Graph-based Sampling with Arbitrary Client Availabilitycited by 10
- 2021From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimizationcited by 8
- 2023Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approachcited by 8
- 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
- 2021Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learningcited by 7
- 2023FederatedTrust: A Solution for Trustworthy Federated Learningcited by 5
- 2022FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devicescited by 4
- 2023Trustworthy Federated Learning: A Surveycited by 4
- 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
- 2024FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learningcited by 4
- 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learningcited by 4
- 2023Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacycited by 3
- 2023A Systematic Literature Review on Client Selection in Federated Learningcited by 3
- 2023Training Latency Minimization for Model-Splitting Allowed Federated Edge Learningcited by 3
- 2023Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learningcited by 2
- 2023FLIPS: Federated Learning using Intelligent Participant Selectioncited by 2
- 2023Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Servicecited by 2
- 2025Hierarchical Federated Learning for Social Network with Mobilitycited by 2
- 2022Scheduling Algorithms for Federated Learning with Minimal Energy Consumptioncited by 1
- 2022MDA: Availability-Aware Federated Learning Client Selectioncited by 1
- 2023Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networkscited by 1
- 2023Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimizationcited by 1
- 2023Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Datacited by 1
- 2024Fairness-Aware Job Scheduling for Multi-Job Federated Learningcited by 1
- 2024Budgeted Online Model Selection and Fine-Tuning via Federated Learningcited by 1
- 2024Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learningcited by 1
- 2025Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimizationcited by 1
- 2025PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learningcited by 1
- 2025Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecturecited by 1
- 2025Fairness in Federated Learning: Fairness for Whom?cited by 1
- 2023Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approachcited by 0
- 2024Distributed client selection with multi-objective in federated learning assisted Internet of Vehiclescited by 0
- 2024Distribution-Free Fair Federated Learning with Small Samplescited by 0
- 2024From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selectioncited by 0
- 2024FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imagingcited by 0
50 most cited of 61 All 61 (JSON)
- 2026Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning3 Aug 2026
- 2026Beyond Passive Aggregation: Active Auditing and Topology-Aware Defense in Decentralized Federated Learning19 Mar 2026
- 2026Federated Learning at the Forefront of Fairness: A Multifaceted Perspective31 Jan 2026
- 2025Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?3 Nov 2025
- 2025Hierarchical Federated Learning for Social Network with Mobility18 Sep 2025
- 2025On the Regularity and Fairness of Combinatorial Multi-Armed Bandit15 Sep 2025
- 2025HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning8 Aug 2025
- 2025Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection31 May 2025
- 2025Fairness in Federated Learning: Fairness for Whom?27 May 2025
- 2025Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture24 Mar 2025
- 2025PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning17 Mar 2025
- 2025Drift-Aware Federated Learning: A Causal Perspective12 Mar 2025
38 more
- 2025MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments3 Mar 2025
- 2025Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization1 Mar 2025
- 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learning18 Jan 2025
- 2024FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning24 Oct 2024
- 2024Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection23 Oct 2024
- 2024A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach16 Sep 2024
- 2024FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging8 Jul 2024
- 2024Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning22 May 2024
- 2024From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection30 Mar 2024
- 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications3 Mar 2024
- 2024Distribution-Free Fair Federated Learning with Small Samples25 Feb 2024
- 2024Budgeted Online Model Selection and Fine-Tuning via Federated Learning19 Jan 2024
- 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Direction10 Jan 2024
- 2024Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles6 Jan 2024
- 2024Fairness-Aware Job Scheduling for Multi-Job Federated Learning5 Jan 2024
- 2023Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data16 Dec 2023
- 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights7 Dec 2023
- 2023Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service5 Dec 2023
- 2023Eliminating Domain Bias for Federated Learning in Representation Space25 Nov 2023
- 2023Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimization31 Aug 2023
- 2023FLIPS: Federated Learning using Intelligent Participant Selection7 Aug 2023
- 2023Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning21 Jul 2023
- 2023Fairness-Aware Client Selection for Federated Learning20 Jul 2023
- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challenges20 Jul 2023
- 2023Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networks19 Jul 2023
- 2023Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning18 Jul 2023
- 2023A Systematic Literature Review on Client Selection in Federated Learning8 Jun 2023
- 2023Trustworthy Federated Learning: A Survey19 May 2023
- 2023Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy9 Apr 2023
- 2023Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach18 Mar 2023
- 2023Fusion of Global and Local Knowledge for Personalized Federated Learning21 Feb 2023
- 2023FederatedTrust: A Solution for Trustworthy Federated Learning20 Feb 2023
- 2023Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach15 Feb 2023
- 2023Recent Advances on Federated Learning: A Systematic Survey3 Jan 2023
- 2022MDA: Availability-Aware Federated Learning Client Selection25 Nov 2022
- 2022FedGS: Federated Graph-based Sampling with Arbitrary Client Availability25 Nov 2022
- 2022Fairness in Federated Learning via Core-Stability3 Nov 2022
- 2022FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices31 Oct 2022
50 newest of 61 All 61 (JSON)
- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
- 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
- 2021Towards Fairness-Aware Federated Learningcited by 57
- 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
- 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
- 2022Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reductioncited by 32
- 2022Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimizationcited by 28
- 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 24
- 2022Fairness in Federated Learning via Core-Stabilitycited by 22
- 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
- 2023Fairness-Aware Client Selection for Federated Learningcited by 15
- 2023Fusion of Global and Local Knowledge for Personalized Federated Learningcited by 14
12 most cited of 60 All 60 (JSON)
- 2026Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning3 Aug 2026
- 2026Federated Learning at the Forefront of Fairness: A Multifaceted Perspective31 Jan 2026
- 2025Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?3 Nov 2025
- 2025Hierarchical Federated Learning for Social Network with Mobility18 Sep 2025
- 2025On the Regularity and Fairness of Combinatorial Multi-Armed Bandit15 Sep 2025
- 2025HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning8 Aug 2025
- 2025Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection31 May 2025
- 2025Fairness in Federated Learning: Fairness for Whom?27 May 2025
- 2025Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture24 Mar 2025
- 2025PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning17 Mar 2025
- 2025Drift-Aware Federated Learning: A Causal Perspective12 Mar 2025
- 2025MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments3 Mar 2025
12 newest of 60 All 60 (JSON)