Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks

TL;DR

This paper explores combining federated learning and edge computing to improve recommendation systems within cloud networks, focusing on efficiency, privacy, and user experience.

Contribution

It introduces a hierarchical federated learning framework and a decentralized caching algorithm using federated deep reinforcement learning to enhance system performance.

Findings

Improved communication efficiency in FL networks.

Enhanced resource utilization at edge servers.

Better user experience through optimized caching strategies.

Abstract

To enable large-scale and efficient deployment of artificial intelligence (AI), the combination of AI and edge computing has spawned Edge Intelligence, which leverages the computing and communication capabilities of end devices and edge servers to process data closer to where it is generated. A key technology for edge intelligence is the privacy-protecting machine learning paradigm known as Federated Learning (FL), which enables data owners to train models without having to transfer raw data to third-party servers. However, FL networks are expected to involve thousands of heterogeneous distributed devices. As a result, communication efficiency remains a key bottleneck. To reduce node failures and device exits, a Hierarchical Federated Learning (HFL) framework is proposed, where a designated cluster leader supports the data owner through intermediate model aggregation. Therefore, based on the improvement of edge server resource utilization, this paper can effectively make up for the limitation of cache capacity. In order to mitigate the impact of soft clicks on the quality of user experience (QoE), the authors model the user QoE as a comprehensive system cost. To solve the formulaic problem, the authors propose a decentralized caching algorithm with federated deep reinforcement learning (DRL) and federated learning (FL), where multiple agents learn and make decisions independently