Federated multi-armed bandits(FMAB)是新的bandit范式,主要灵感来源于cognitive radio 和recommender systems的实际应用场景。这篇论文提出了一个通用型FMAB框架,并研究了该框架下的两种模型。 首先研究了近似模型,在该近似模型中,不同的local model都是global model 的服从于一个未知分布的随机实现。在这个近似模型中,...
Url: ojs.aaai.org/index.php/ Abstract: We study the problem of best arm identification in a federated learning multi-armed bandit setup with a central server and multiple clients. Each client is associated with a multi-armed bandit in which each arm yields i.i.d. rewards following a Gaussi...
the multi-armed bandit (Yoshida et al.2020) method was used to reduce the exploration and the exploitation and trade off in the mobile network. This method helps manage uncertainty caused by a huge amount of data, and one of the main advantages ...
Aggregation Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning Incentive A Comprehensive Survey of Incentive Mechanism for Federated Learning A Survey of Incentive Mechanism Design for Federated Learning Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic...
Based on this fact, in this paper, we design a multi-armed-bandit (MAB)-based edge scheduling scheme to improve the training efficiency and reduce the latency for FL within IoV. Particularly, considering a high requirement of security for IoV related wireless services, we further design the ...
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning KAUST Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning The Ohio State University FedAvg with Fine Tuning: Local Updates Lead to Representation Learning The University...
FeDXL: Provable Federated Learning for Deep X-Risk Optimization [pdf] [code] FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction [pdf] [code] One-Shot Federated Conformal Prediction [pdf] [code] Revisiting Weighted Aggregation in Federated Learning with Neural...
Automated Collaborator Selection for Federated Learning with Multi-Armed Bandit Agents. In Proceedings of the 4th FlexNets Workshop on Flexible Networks Artificial Intelligence Supported Network Flexibility and Agility, Virtual Event, 23 August 2021. [Google Scholar] [CrossRef] Ji, S.; Pan, S.; ...
This approach identifies client clusters with data distributions closely resembling IID characteristics and employs multi-armed bandit techniques to select clusters with the fastest convergence rates, thereby enhancing the overall efficiency of federated learning. Younis et al. [22] proposed a new method ...
Lai et al. [14] proposed Oort, which abstracts node selection as a multi-armed bandit (MAB) problem and designs a RL-based node selection algorithm to improve the accuracy of selecting higher quality data nodes while reducing the number of training rounds in FL. However, these methods do ...