Federated Learning (FL) has emerged as a crucial paradigm for training machine learning (ML) models on decentralized edge devices while addressing data privacy. In FL, models are collaboratively trained across multiple clients without sharing raw data, addressing privacy concerns while enabling collaborative ML. In this talk, I will delve into the myriad practical challenges encountered in the implementation of FL, and present my research contributions aimed at overcoming these obstacles.

Drawing inspiration from diverse fields like information theory, data privacy, and optimization, my research works offer innovative solutions to advance the state-of-the-art in FL. These contributions are poised to make privacy-preserving large-scale ML a practical reality. Furthermore, my talk will encompass my recent research focusing on ‘exact machine unlearning’ for federated clustering (FC), addressing privacy concerns even ‘after’ model training. This novel perspective has gained significance in the wake of emerging data privacy laws and the ‘right to be forgotten’, particularly for sensitive data like medical and genomic records.