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 ...
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...
We consider a $K$ -armed bandit problem in general graphs where agents are arbitrarily connected and each of them has limited memorizing capabilities and ... F Li,X Yuan,LX Cheng - IEEE/ACM Transactions on Networking: A Joint Publication of the IEEE Communications Soceity, the IEEE Computer ...
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...
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.; ...
The study in [72] proposed a fairness-enhanced scheduling mechanism based on a multiarmed bandit algorithm, where device selection was guided by a weighted reward function that balances model freshness and energy consumption. This multicriteria scheduling promotes fair participation among ground devices,...