On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning

Abstract--In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding client access and diversifying data enhance models by incorporating diverse perspectives, thereby enhancing adaptability. However, challenges arise in dynamic and mobile environments where certain devices may become inaccessible as FL clients, impacting data availability and client selection methods. To address this, we propose an On-Demand solution, deploying new clients using Docker Containers on-the-fly. It employs an autonomous end-to-end solution for handling model deployment and client selection. Simulated tests show that our architecture can easily adjust to changes in the environment and respond to On-Demand requests. FL can enhance traffic prediction models using realtime data from vehicles moving on the road. Regulation in the European Union, aim to protect data privacy One of the main limitations in existing FL frameworks [1]. However, the stringency of these regulations varies is in accessing the full potential of available data due to globally. A study [2] revealed a notable increase in privacy reliance on static clients, leading to incomplete or biased requests from 2021 to 2022, indicating growing concerns about dataset representations and affecting model performance. Access and Deletion requests saw a today's digital landscape, acquiring more clients is about substantial peak, with a 72% year-over-year increase in data efficiency.