Distributed Machine Learning over Wireless Networks

Table of Contents

Seminars-2-2025 - This article is part of a series.

Part 1: Open RAN: Technological Foundations and Market Dynamics

Part 2: The Dawn of Low Latency and the Death of Bandwidth

Part 3: Performance Analysis of Lightweight Container Orchestration Platforms for Edge-Based IoT Applications

Part 4: Realizing the Network Closed Loop Automation

Part 5: 5 Key Pillars of Telco Cloud Evolution

Part 6: Leading Change: How Tesla is Transforming the Business Model of the Automotive Industry

Part 7: When Confidential Isn’t Secure: Fingerprinting Attacks on Trusted Execution Environments

Part 8: This Article

Abstract #

The future of machine learning services is intricately tied to wireless networks, with a growing emphasis on distributed devices lacking wired connections. This seminar delves into the critical role of distributed machine learning over wireless networks, addressing the inefficiencies of traditional wireless protocols and centralized machine learning methods. This creates the need for new wireless communication methods, specifically on the medium access control and physical layers that will be arguably included in 6G. The seminar provides fundamental insights and practical application scenarios for these evolving learning methods and wireless technologies.

Bio #

José Mairton B. da Silva Jr. is currently an Assistant Professor in the Department of Information Technology at Uppsala University, Sweden. He received his Ph.D. from KTH Royal Institute of Technology, Stockholm, Sweden, in 2019. From 2019 to 2021, he was a postdoctoral researcher at KTH Royal Institute of Technology, Stockholm, Sweden. Between 2022 and 2023, he was a Marie Skłodowska-Curie Postdoctoral Fellow with Princeton University, USA, and KTH Royal Institute of Technology, Sweden. He received his B.Sc. (Hons.) and M.Sc. degrees in Teleinformatics Engineering from the Federal University of Ceará, Brazil, in 2012 and 2014, respectively. He currently serves as the Workshops, Tutorials, & Symposia Officer of the IEEE Communications Society Emerging Technology Initiative on Machine Learning for Communications. He has been actively involved in the organization of several IEEE international conferences and workshops, including serving as co-chair for GLOBECOM 2025, ICMLCN 2024, and SECON 2022–2023. He gave several tutorials at major IEEE flagship conferences, including ICASSP, PIMRC, ICC, and GLOBECOM. In 2021, he was recognized as an Exemplary Reviewer for the IEEE Open Journal of the Communications Society. His research interests include distributed machine learning and optimization over wireless communications.