April 14 – April 19
The “Timely and Private Machine Learning over Networks” is a workshop (WS6) organized for the IEEE ICASSP 2024 conference in Seul, South Korea.
Machine learning over networked systems, e.g., distributed/federated learning, is envisioned as the bedrock of future intelligent Internet-of-Things. By exploiting the computing power of end-user devices and inter-node communications, agents can exchange information with each other to collaboratively train a statistical model without centralizing their private data, which also contributes to the development of trustworthy intelligent systems. Despite its great potential, several new challenges must be addressed to make this paradigm possible. Specifically, in many applications, the parameters/states to be learned at different agents vary over time. And owing to impacts from data processing time, communication bandwidth, and transmission errors, the parameters delivered from one agent to the others may not be fresh. On the one hand, the stalled information impedes the performance of a distributed learning system, especially for real-time applications. On the other hand, the corrupted and stalled information improves end-users’ privacy, as instantaneous, accurate information is inaccessible. To that end, this workshop aims to foster discussion, discovery, and dissemination of novel ideas and approaches in the interplay between timeliness and privacy in machine learning over networks.