Distributed, Private, and Robust Machine Learning over Networks

Distributed machine learning is rapidly advancing research field that lies at the intersection of artificial intelligence, edge computing, and large-scale networked systems. As sheer data volumes are being generated on edge devices, such as mobile phones, tablets/laptops, or autonomous vehicles, there is a particular trend in distributed ML, e.g., federated learning, that moves the ML model directly onto the data generating sources, i.e., the end-users, for on-device data processing. This approach not only substantially reduces the communication overheads but, more importantly, facilitates the end-users to obtain a global model without centralizing their private data, thereby contributing to the development of trustworthy intelligent systems. Despite its great potential, several new challenges need to be addressed to make this paradigm possible. Specifically, the processing power, communication capability, and data quality across different end-user devices are highly heterogeneous, giving rise to significant fluctuation, or even divergence, in the learning process. Moreover, even if the end-users only exchange intermediate model parameters, it does not rule out the threat of privacy leakage as a malicious agent can adopt advanced inference techniques to recover a large portion of origin information from an intermediate parameter. To that end, this workshop aims to foster discussion, discovery, and dissemination of novel ideas and approaches for private and robust distributed machine learning. We solicit high-quality original papers on topics including, but not limited to:

Robust distributed learning algorithms against data heterogeneity as well as system heterogeneity
Privacy enhancing schemes (e.g., adopting differential privacy or creating synthetic data) for distributed learning systems
Novel methods for distributed machine learning with limited communication resources
Over-the-air computation for private and robust distributed machine learning systems
Impact of network topology on distributed machine learning algorithms
Robust and Private distributed reinforcement/meta/deep learning and other novel learning paradigms
Networking protocols to improve robustness and privacy in distributed learning
Experimental implementations and testbeds on large-scale distributed learning systems

Workshop Organizers

Howard H. Yang, ZJU-UIUC Institute, Zhejiang University, China
Nikolaos Pappas, Department of CIS, Linköping University, Sweden
Chaoqun You, ISTD Pillar, Singapore University of Technology and Design, Singapore
Ahmed Arafa, ECE Department, University of North Carolina at Charlotte, USA

Contribution format and workshop deadlines

The contributing formats to this workshop will be short papers that have a 6-page limit, covering recent advances in distributed machine learning with a particular focus on techniques that enhances the robustness and privacy of the system.
The updated submission deadline for this workshop is June 1st, 2023.

Paper Submission Link

Workshop papers can be submitted via the following link:
Paper_submission_link

Schedule

2:30 — 2: 40 pm

Openning Remarks (Prof. Nikolaos Pappas)
‍
Session 1 (Session Chair: Prof. Nikolaos Pappas)

2:40 — 3:00 pm

Obtaining Security Characteristics through Simultaneous Reception of LoRaWAN Packets in Redundant Gateways

3:00 — 3:20 pm

Grid-Level Performance Evaluation for 5G Vertical Networks with High Resolution: A Deep Learning-Based Scheme

3:20 — 3:40 pm

Federated Learning with Partial Gradients Over-the-Air

3:40 — 4:00 pm

Generative Adversarial Network-Based Network Anomaly Detection with Unlabeled Data

‍Coffee Break (4:00 - 4:30 pm)
‍
Session 2 (Session Chair: Prof. Nikolaos Pappas)
4:00 — 4:20 pm

Privacy-Preserving Mobility-Aware Federated Collaborative Filtering Framework for Caching Prediction in Vehicular Networks