mlsanityData to 7 Oct 2026

Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization

arXiv 2201.07912 ↗First posted 19 Jan 2022cs.LG cs.DC cs.ITcited by 28

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Abstract (arXiv)

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users' local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless environment; however, training models in a time-efficient manner can be a challenging task due to intermittent connectivity of devices, heterogeneous connection quality, and non-i.i.d. data. In this paper, we provide a novel convergence analysis of non-convex loss functions using FL on both i.i.d. and non-i.i.d. datasets with arbitrary device selection probabilities for each round. Then, using the derived convergence bound, we use stochastic optimization to develop a new client selection and power allocation algorithm that minimizes a function of the convergence bound and the average communication time under a transmit power constraint. We find an analytical solution to the minimization problem. One key feature of the algorithm is that knowledge of the channel statistics is not required and only the instantaneous channel state information needs to be known. Using the FEMNIST and CIFAR-10 datasets, we show through simulations that the communication time can be significantly decreased using our algorithm, compared to uniformly random participation.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 26 of its 33 references that are in the map. First posted on arXiv 19 Jan 2022.

Builds on

26 of 33 references.

References · 26

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2018LEAF: A Benchmark for Federated Settingscited by 780
  7. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  8. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  9. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  10. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  11. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  12. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
14 more
  1. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  2. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 192
  3. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  4. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  5. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  6. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 80
  7. 2021Fast Federated Learning in the Presence of Arbitrary Device Unavailabilitycited by 72
  8. 2020Towards Flexible Device Participation in Federated Learningcited by 64
  9. 2019Active Federated Learningcited by 63
  10. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  11. 2020An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guaranteecited by 61
  12. 2020Communication-Efficient Federated Learning via Optimal Client Samplingcited by 39
  13. 2020Optimal Gradient Compression for Distributed and Federated Learningcited by 37
  14. 2020Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocationcited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 7; 2024: 11; 2025: 4; 2026: 1.

28 papers.

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  1. 2022DELTA: Diverse Client Sampling for Fasting Federated Learningcited by 24
  2. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  3. 2022Federated Learning with Flexible Controlcited by 12
  4. 2022A Survey on Participant Selection for Federated Learning in Mobile Networkscited by 11
  5. 2023Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Modelscited by 10
  6. 2023MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updatescited by 10
  7. 2024Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailabilitycited by 9
  8. 2022Federated Learning Using Variance Reduced Stochastic Gradient for Probabilistically Activated Agentscited by 8
  9. 2023Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participationcited by 8
  10. 2023Federated Learning While Providing Model as a Service: Joint Training and Inference Optimizationcited by 8
  11. 2023Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communicationscited by 6
  12. 2022Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learningcited by 3
16 more
  1. 2023Network Adaptive Federated Learning: Congestion and Lossy Compressioncited by 3
  2. 2024Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamicscited by 3
  3. 2024Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compressioncited by 2
  4. 2026The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statisticscited by 2
  5. 2023A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averagingcited by 1
  6. 2024Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codescited by 1
  7. 2024Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Samplingcited by 1
  8. 2024FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learningcited by 1
  9. 2025Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimizationcited by 1
  10. 2025Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edgecited by 1
  11. 2025Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learningcited by 1
  12. 2025Adaptive Federated LoRA in Heterogeneous Wireless Networks with Independent Samplingcited by 1
  13. 2024FLASH: Federated Learning Across Simultaneous Heterogeneitiescited by 0
  14. 2024From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selectioncited by 0
  15. 2024Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learningcited by 0
  16. 2024Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learningcited by 0