Timely and Private Machine Learning over Networks
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. We solicit high-quality original papers on topics including, but not limited to:
Workshop Organizers
Sunday, April 14th, 2024
08:30 — 12:00 South Korea Time
Location: Room 209A

Session 1 (Session chair: Prof. Nikolaos Pappas)

08:30 - 08:45 am
Opening Remarks

08:45 - 09:00 am
Towards Efficient Backdoor Attacks Against Federated Self-Supervised Learning Through Intra-Union Aggregation

09:00 - 09:15 am
Convergence Analysis of Semi-Federated Learning with Non-IID Data

09:15 - 09:30 am
A Compressed Decentralized Federated Learning Framework for Enhanced Environmental Awareness in V2V Networks

09:30 - 09:45 am
Over the Air Federated Learning in the Presence of Impulsive Noise

09:45 - 10:15 am
Coffee Break

Session 2 (Session chair: Prof. Nikolaos Pappas)

10:15 - 10:30 am
FedRF-Adapt: Robust and Communication-Efficient Federated Domain Adaptation via Random Features

10:30 - 10:45 am
Recommendation Algorithm Based on Federated Multi-modal Learning

10:45 - 11:00 am
Version Age-Based Client Scheduling Policy for Federated Learning

11:00 - 11:15 am
PerSOTA FL: A Robust-to-Noise Personalized Over the Air Federated Learning for Human Activity Recognition

11:15 - 11:30 am
Towards Collaborative Multi-modal Federated Learning for Human Activity Recognition in Smart Workplace Environments

11:30 - 11:45 am
Byzantine-Resilient Hierarchical Federated Learning with Clustered Over-the-Air Aggregation

Submission Guidelines
Manuscripts should conform to the ICASSP paper format as stated in the main conference.
Important Dates
Workshop Paper Submission Deadline: December 31, 2023 (23:59 AoE)
Workshop Paper Acceptance Notification: January 31, 2024 (23:59 AoE)
Workshop Camera Ready Paper Deadline: February 10, 2024 (23:59 AoE)
Paper Submission Link
Workshop papers can be submitted via the following link: