对于clientm和client()n(m≠n)他们选择同样的armk分别到的rewardX_{k,m},X_{k,n}来自于不同的分布:X_{k,m}\sim N(\mu_{k,m}, \sigma)和X_{k,n}\sim N(\mu_{k,n}, \sigma),也就是(Non-IID)。 (2)server:在central server上有一个global stochastic MAB模型,这个MAB的arm也是上述K个...
Each client is associated with a multi-armed bandit in which each arm yields i.i.d. rewards following a Gaussian distribution with an unknown mean and known variance. The set of arms is assumed to be the same at all the clients. We define two notions of best arm local and global. The...
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 ...
Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Network 来自 arXiv.org 喜欢 0 阅读量: 6 作者:H Tu,L Chen,Z Li,X Chen,W Wu 摘要: In this paper, we study a vehicle selection problem for federated learning (FL) over vehicular networks. Specifically, we ...
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...
To tackle these problems, we first propose a novel client selection method based on Multi-Armed Bandit (MAB). The method uses the historical training information uploaded by each client to calculate its correlation and contribution. The calculated values are then used to select a set of clients ...
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 ...
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...
4. Multi-Armed Bandit (MAB) for Optimal MPTCP Scheduling In this section, we first provide a short background of MAB and then employ MAB to model and solve the scheduling problem (11). Next, we improve MAB’s efficiency with federated learning and opportunistic scheduling. MAB employs a seq...
4. Multi-Armed Bandit (MAB) for Optimal MPTCP Scheduling In this section, we first provide a short background of MAB and then employ MAB to model and solve the scheduling problem (11). Next, we improve MAB’s efficiency with federated learning and opportunistic scheduling. MAB employs a seq...