most citedAre You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark

2 citations · 6 across the 5 of their papers we have counts for

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cs.CR2023

FedDefender: Client-Side Attack-Tolerant Federated Learning

Sungwon Park, Sungwon Han, Fangzhao Wu +4

Federated learning enables learning from decentralized data sources without compromising privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning a…

cs.CR2022★ 2 cited

Robust Quantity-Aware Aggregation for Federated Learning

Jingwei Yi, Fangzhao Wu, Huishuai Zhang +4

Federated learning (FL) enables multiple clients to collaboratively train models without sharing their local data, and becomes an important privacy-preserving machine learning fram…