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Başak Güler

2 accepted papers

2023

Dropout-Resilient Secure Multi-Party Collaborative Learning with Linear Communication Complexity

AISTATS 2023poster

Collaborative machine learning enables privacy-preserving training of machine learning models without collecting sensitive client data. Despite recent breakthroughs, communication bottleneck is still a major challenge against its scalability to larger networks. To address this challenge, we propose…

Cited by 5SourcePDFScholar
2023

Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning

AAAI 2023technical

Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the privacy of individual users in a single training ro…

Cited by 101SourcePDFScholar