mlsanityData to 14 Oct 2026

Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning

arXiv 2405.04122 ↗First posted 7 May 2024cs.LG cs.DCcited by 2

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Abstract (arXiv)

Federated Learning (FL) enables multiple devices to collaboratively train a shared model while ensuring data privacy. The selection of participating devices in each training round critically affects both the model performance and training efficiency, especially given the vast heterogeneity in training capabilities and data distribution across devices. To address these challenges, we introduce a novel device selection solution called FedRank, which is an end-to-end, ranking-based approach that is pre-trained by imitation learning against state-of-the-art analytical approaches. It not only considers data and system heterogeneity at runtime but also adaptively and efficiently chooses the most suitable clients for model training. Specifically, FedRank views client selection in FL as a ranking problem and employs a pairwise training strategy for the smart selection process. Additionally, an imitation learning-based approach is designed to counteract the cold-start issues often seen in state-of-the-art learning-based approaches. Experimental results reveal that model~ boosts model accuracy by 5.2% to 56.9%, accelerates the training convergence up to 2.01 × and saves the energy consumption up to 40.1%.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 21 of its 46 references that are in the map. First posted on arXiv 7 May 2024.

Builds on

21 of 46 references.

References · 21

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9 more
  1. 2017Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Predictioncited by 93
  2. 2019Active Federated Learningcited by 63
  3. 2021AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learningcited by 39
  4. 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 31
  5. 2023BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Drivingcited by 28
  6. 2020An Imitation Learning Approach for Cache Replacementcited by 24
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  9. 2024Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Trainingcited by 10

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Cited by year

Citing papers per year: 2024: 2.

2 papers.

  1. 2024FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimizationcited by 9
  2. 2024Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator
  2. 2025Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
  3. 2021Federated Learning with Downlink Device Selection
  4. 2025Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence
  5. 2026Conflict-Aware Client Selection for Multi-Server Federated Learning
  6. 2026Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention
  7. 2024FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
  8. 2026FANS: Federated Adaptive Network Search Learning for Heterogeneous Devices