Fast-Convergent Federated Learning
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Abstract (arXiv)
Federated learning has emerged recently as a promising solution for distributing machine learning tasks through modern networks of mobile devices. Recent studies have obtained lower bounds on the expected decrease in model loss that is achieved through each round of federated learning. However, convergence generally requires a large number of communication rounds, which induces delay in model training and is costly in terms of network resources. In this paper, we propose a fast-convergent federated learning algorithm, called FOLB, which performs intelligent sampling of devices in each round of model training to optimize the expected convergence speed. We first theoretically characterize a lower bound on improvement that can be obtained in each round if devices are selected according to the expected improvement their local models will provide to the current global model. Then, we show that FOLB obtains this bound through uniform sampling by weighting device updates according to their gradient information. FOLB is able to handle both communication and computation heterogeneity of devices by adapting the aggregations according to estimates of device's capabilities of contributing to the updates. We evaluate FOLB in comparison with existing federated learning algorithms and experimentally show its improvement in trained model accuracy, convergence speed, and/or model stability across various machine learning tasks and datasets.
Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jul 2020.
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Citing papers per year: 2021: 12; 2022: 14; 2023: 13; 2024: 11; 2025: 8; 2026: 3.
61 papers.
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- 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
- 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
- 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
- 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
- 2021DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learningcited by 40
- 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
- 2022On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyondcited by 37
- 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
- 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
- 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
- 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
- 2021Communication-Efficient Online Federated Learning Framework for Nonlinear Regressioncited by 16
38 more
- 2021Decentralized Unsupervised Learning of Visual Representationscited by 15
- 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
- 2021Efficient Federated Meta-Learning over Multi-Access Wireless Networkscited by 13
- 2023Federated Learning with Regularized Client Participationcited by 13
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
- 2024Green Federated Learning: A new era of Green Aware AIcited by 12
- 2022Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholdscited by 9
- 2024Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networkscited by 9
- 2022To Store or Not? Online Data Selection for Federated Learning with Limited Storagecited by 7
- 2022Addressing Heterogeneity in Federated Learning via Distributional Transformationcited by 6
- 2024Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspectivecited by 5
- 2022Client Selection for Federated Bayesian Learningcited by 4
- 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selectioncited by 4
- 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
- 2023Adaptive Parameterization of Deep Learning Models for Federated Learningcited by 3
- 2023Adaptive Federated Learning via New Entropy Approachcited by 3
- 2023Resource-Efficient Federated Hyperdimensional Computingcited by 3
- 2023FedCode: Communication-Efficient Federated Learning via Transferring Codebookscited by 3
- 2021On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edgecited by 2
- 2022Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computingcited by 2
- 2023Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentationcited by 2
- 2024On the Convergence of Federated Learning Algorithms without Data Similaritycited by 2
- 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustmentscited by 2
- 2025Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learningcited by 2
- 2022Efficient Distributed DNNs in the Mobile-edge-cloud Continuumcited by 1
- 2022Matching Pursuit Based Scheduling for Over-the-Air Federated Learningcited by 1
- 2022DP2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoringcited by 1
- 2023Prototype of deployment of Federated Learning with IoT devicescited by 1
- 2023Mitigating Data Injection Attacks on Federated Learningcited by 1
- 2024Federated Learning Can Find Friends That Are Advantageouscited by 1
- 2024FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networkscited by 1
- 2024Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Designcited by 1
- 2025Client Selection in Federated Learning with Data Heterogeneity and Network Latenciescited by 1
- 2022Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descentcited by 0
- 2022Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggeringcited by 0
- 2023Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradientcited by 0
- 2023Utilizing Free Clients in Federated Learning for Focused Model Enhancementcited by 0
- 2023Tackling Heterogeneity in Medical Federated learning via Vision Transformerscited by 0
50 most cited of 61 All 61 (JSON)
- 2026Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data13 Sep 2026
- 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling8 Sep 2026
- 2026Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks12 Apr 2026
- 2025FedLAM: Low-latency Wireless Federated Learning via Layer-wise Adaptive Modulation9 Oct 2025
- 2025From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks3 Sep 2025
- 2025Cooperative SGD with Dynamic Mixing Matrices20 Aug 2025
- 2025PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning18 Jun 2025
- 2025Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning16 May 2025
- 2025Client Selection in Federated Learning with Data Heterogeneity and Network Latencies2 Apr 2025
- 2025XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing16 Mar 2025
- 2025Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology13 Feb 2025
- 2024Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective12 Nov 2024
38 more
- 2024Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design8 Oct 2024
- 2024Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning29 Sep 2024
- 2024Green Federated Learning: A new era of Green Aware AI19 Sep 2024
- 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective30 May 2024
- 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustments8 May 2024
- 2024FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks23 Apr 2024
- 2024Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks22 Apr 2024
- 2024GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning26 Mar 2024
- 2024On the Convergence of Federated Learning Algorithms without Data Similarity29 Feb 2024
- 2024Federated Learning Can Find Friends That Are Advantageous7 Feb 2024
- 2023Mitigating Data Injection Attacks on Federated Learning4 Dec 2023
- 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selection28 Nov 2023
- 2023Prototype of deployment of Federated Learning with IoT devices24 Nov 2023
- 2023FedCode: Communication-Efficient Federated Learning via Transferring Codebooks15 Nov 2023
- 2023Tackling Heterogeneity in Medical Federated learning via Vision Transformers13 Oct 2023
- 2023Utilizing Free Clients in Federated Learning for Focused Model Enhancement6 Oct 2023
- 2023Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation29 Sep 2023
- 2023Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient17 Aug 2023
- 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency20 Jul 2023
- 2023Resource-Efficient Federated Hyperdimensional Computing2 Jun 2023
- 2023Adaptive Federated Learning via New Entropy Approach27 Mar 2023
- 2023Federated Learning with Regularized Client Participation7 Feb 2023
- 2023Adaptive Parameterization of Deep Learning Models for Federated Learning6 Feb 2023
- 2022Client Selection for Federated Bayesian Learning11 Dec 2022
- 2022Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering23 Nov 2022
- 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunities3 Nov 2022
- 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles27 Oct 2022
- 2022Addressing Heterogeneity in Federated Learning via Distributional Transformation26 Oct 2022
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directions24 Oct 2022
- 2022Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descent7 Oct 2022
- 2022To Store or Not? Online Data Selection for Federated Learning with Limited Storage1 Sep 2022
- 2022DP2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring30 Jun 2022
- 2022Matching Pursuit Based Scheduling for Over-the-Air Federated Learning14 Jun 2022
- 2022On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond10 Jun 2022
- 2022Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds7 Apr 2022
- 2022Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing23 Mar 2022
- 2022Efficient Distributed DNNs in the Mobile-edge-cloud Continuum23 Feb 2022
- 2021On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge21 Dec 2021
50 newest of 61 All 61 (JSON)
- 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
- 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
- 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
- 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
- 2021DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learningcited by 40
- 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
- 2022On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyondcited by 37
- 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
- 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
- 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
- 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
- 2021Communication-Efficient Online Federated Learning Framework for Nonlinear Regressioncited by 16
12 most cited of 61 All 61 (JSON)
- 2026Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data13 Sep 2026
- 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling8 Sep 2026
- 2026Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks12 Apr 2026
- 2025FedLAM: Low-latency Wireless Federated Learning via Layer-wise Adaptive Modulation9 Oct 2025
- 2025From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks3 Sep 2025
- 2025Cooperative SGD with Dynamic Mixing Matrices20 Aug 2025
- 2025PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning18 Jun 2025
- 2025Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning16 May 2025
- 2025Client Selection in Federated Learning with Data Heterogeneity and Network Latencies2 Apr 2025
- 2025XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing16 Mar 2025
- 2025Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology13 Feb 2025
- 2024Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective12 Nov 2024
12 newest of 61 All 61 (JSON)