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Found 6 papers, 0 papers with code
On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning
In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation.
CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication
In this study, we propose combining these technologies to address the continuous authentication challenge while protecting user privacy and limiting device resource usage.
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions.
ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme
In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process.
Reward Shaping Using Convolutional Neural Network
Due to (1) the previous success of using message passing for reward shaping; and (2) the CNN planning behavior, we use these messages to train the CNN of VIN-RS.
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing
Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications.
