In-Network Processing for Timely Federated Multi-Task Learning over Wireless Edges
This project is led by NC State.
This project, led by NC State, will enable data processing to occur in the middle of 5G wireless transmissions, forging a connection between communication, computing and learning. It will also provide a privacy-preserving framework to protect decentralized data. Low-end control units will be able to run sophisticated and heavy-loaded machine learning algorithms, while freeing up resources for the multi-agents. Additionally, the proposed solution can offload computation to the networking infrastructure. This project will deliver improved link bandwidths, computing power, and protection for data privacy.
Sponsor
Cisco Systems, Inc.
The grant—running from August 1, 2021 to December 31, 2022—is for a total of $175,001.
Principle Investigators
Shih-Chun Lin
More Details
This project will employ in-network computation at 5G wireless edges and enable data processing to occur in the middle of the transmissions between multi-agents and remote cloud servers. Thus, it forges effective convergence of communication, computing, and learning with regard to wireless link bandwidths, available computing power, and collected data's statistical features. Also, this project will provide a privacy-preserving framework from decentralized data by exploiting in-network processing operations. The proposed framework can achieve sophisticated and heavy-loaded machine learning algorithms through multiple low-end control units. It, in turn, preserves the data privacy for massive mobile user information in ICT or sensing information and intelligent manufacture/control commands in industrial scenarios. On the other hand, the proposed solution can also offload computation to the networking infrastructure, releasing the burden of multi-agents.