mlsanityData to 13 Oct 2026

Context-Aware Online Client Selection for Hierarchical Federated Learning

arXiv 2112.00925 ↗First posted 2 Dec 2021cs.LG cs.AI cs.DCcited by 20

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Abstract (arXiv)

Federated Learning (FL) has been considered as an appealing framework to tackle data privacy issues of mobile devices compared to conventional Machine Learning (ML). Using Edge Servers (ESs) as intermediaries to perform model aggregation in proximity can reduce the transmission overhead, and it enables great potentials in low-latency FL, where the hierarchical architecture of FL (HFL) has been attracted more attention. Designing a proper client selection policy can significantly improve training performance, and it has been extensively used in FL studies. However, to the best of our knowledge, there are no studies focusing on HFL. In addition, client selection for HFL faces more challenges than conventional FL, e.g., the time-varying connection of client-ES pairs and the limited budget of the Network Operator (NO). In this paper, we investigate a client selection problem for HFL, where the NO learns the number of successful participating clients to improve the training performance (i.e., select as many clients in each round) as well as under the limited budget on each ES. An online policy, called Context-aware Online Client Selection (COCS), is developed based on Contextual Combinatorial Multi-Armed Bandit (CC-MAB). COCS observes the side-information (context) of local computing and transmission of client-ES pairs and makes client selection decisions to maximize NO's utility given a limited budget. Theoretically, COCS achieves a sublinear regret compared to an Oracle policy on both strongly convex and non-convex HFL. Simulation results also support the efficiency of the proposed COCS policy on real-world datasets.

Key facts. Cited by 20 papers in the map (data through 13 Oct 2026). Builds on 25 of its 58 references that are in the map. First posted on arXiv 2 Dec 2021.

Builds on

25 of 58 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,470
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,227
  4. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  5. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 350
  6. 2018Federated Learning via Over-the-Air Computationcited by 219
  7. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  8. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 193
  9. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 107
  10. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  11. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 95
  12. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
13 more
  1. 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
  2. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  3. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  4. 2020An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guaranteecited by 62
  5. 2020Fast-Convergent Federated Learningcited by 61
  6. 2020Multi-Armed Bandit Based Client Scheduling for Federated Learningcited by 56
  7. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 52
  8. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  9. 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 25
  10. 2020Adaptive Federated Learning With Gradient Compression in Uplink NOMAcited by 19
  11. 2021Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradientscited by 13
  12. 2019Budget-constrained Edge Service Provisioning with Demand Estimation via Bandit Learningcited by 5
  13. 2019Throughput of CDM-based Random Access With SINR Capturecited by 1

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 7; 2025: 5; 2026: 2.

20 papers.

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  1. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  2. 2022DELTA: Diverse Client Sampling for Fasting Federated Learningcited by 24
  3. 2021FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximationcited by 10
  4. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  5. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  6. 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approachcited by 6
  7. 2023A Systematic Literature Review on Client Selection in Federated Learningcited by 3
  8. 2023Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Servicecited by 2
  9. 2025Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoTcited by 2
  10. 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guaranteescited by 2
  11. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  12. 2024Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasetscited by 1
8 more
  1. 2024A Hierarchical Federated Learning Approach for the Internet of Thingscited by 0
  2. 2024GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learningcited by 0
  3. 2024A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approachcited by 0
  4. 2024Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learningcited by 0
  5. 2025Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodologycited by 0
  6. 2025Semi-overlapping Multi-bandit Best Arm Identification for Sequential Support Network Learningcited by 0
  7. 2026Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learningcited by 0
  8. 2026SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning Systemcited by 0