Tools and Techniques for Privacy-aware, Edge-centric Distributed Deep Learning
Ziran Min, Robert E. Canady, Uttam Ghosh, Aniruddha S. Gokhale, Akram Hakiri · 2020
Training and inferencing phases of Deep Learning (DL) are compute-intensive that require substantial amount of cloud-hosted resources. However, real-time needs of some edge-based applications as well as the variable and wildly fluctuating edge-cloud latency require new ways to exploit clusters of edge devices in a decentralized and federated manner that perform on-device or edge-based DL training/inferencing. However, edge-based DL is fraught with many challenges including the need to discover the right resources, heterogeneity in resource types leading to non-uniform execution times among cluster members, increased incidences of failures and network disconnectivity leading to consistency issues, preserving privacy of data used in DL tasks, the type of distributed DL algorithm used and its performance on the chosen resources, and many others. To address this plethora of challenges, this paper proposes solutions, which include privacy-preserving, inferencing on heterogeneous edge devices, and CAP-driven trade-offs and Blockchain-based consensus.