Advancing Edge Computing with Federated Deep Learning: Strategies and Challenges

Abstract: Edge computation (EC) represents a transformative architecture in which cloud computing services are decentralized to the locations where data originates. This shift has been facilitated by the integration of deep learning (DL) technologies, notably in eliminating latency issues commonly referred to as the "echo effect" across various platforms. In typical EC-enabled DL frameworks where data producers are directly involved, it is often necessary to share data with third parties or edge/cloud servers to facilitate model training. This process, however, raises significant concerns regarding synchronization with high data rates, seamless migration, and security, consequently exposing the system to privacy vulnerabilities. These challenges can be addressed through the adoption of Federated Learning (FL), which provides a robust mechanism to mitigate risks associated with data loss, ensure data freshness, and enhance privacy.