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

arXiv: https://arxiv.org/abs/2405.04122 · HTML: https://mlsanity.com/paper/2405.04122/

## 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 (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://mlsanity.com/paper/2010.01264/) (2020)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [Reinforcement and Imitation Learning via Interactive No-Regret Learning](https://mlsanity.com/paper/1406.5979/) (2014)
- [TiFL: A Tier-based Federated Learning System](https://mlsanity.com/paper/2001.09249/) (2020)
- [Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction](https://mlsanity.com/paper/1703.01030/) (2017)
- [Active Federated Learning](https://mlsanity.com/paper/1909.12641/) (2019)
- [AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning](https://mlsanity.com/paper/2107.08147/) (2021)
- [A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning](https://mlsanity.com/paper/2201.02932/) (2022)
- [BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2312.06371/) (2023)
- [An Imitation Learning Approach for Cache Replacement](https://mlsanity.com/paper/2006.16239/) (2020)
- [Optimal Completion Distillation for Sequence Learning](https://mlsanity.com/paper/1810.01398/) (2018)
- [Federated Noisy Client Learning](https://mlsanity.com/paper/2106.13239/) (2021)
- [Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training](https://mlsanity.com/paper/2404.13349/) (2024)

## Built on by (most cited first, 2 of 2)

- [FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization](https://mlsanity.com/paper/2405.06312/) (2024)
- [Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning](https://mlsanity.com/paper/2405.13584/) (2024)

## 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%.