354 citations · 1.4k across the 34 of their papers we have counts for
9 papers · 1 filter
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
Sara Babakniya, Zalan Fabian, Chaoyang He +2
Deep learning models often suffer from forgetting previously learned information when trained on new data. This problem is exacerbated in federated learning (FL), where the data is…
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification
Shanshan Han, Wenxuan Wu, Baturalp Buyukates +4
Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks. Existing defense mechanisms face critical limitations…
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Sara Babakniya, Zalan Fabian, Chaoyang He +2
Deep learning models are prone to forgetting information learned in the past when trained on new data. This problem becomes even more pronounced in the context of federated learnin…
Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data
Jiaqi Shao, Shanshan Han, Chaoyang He +1
Federated heavy-hitter analytics involves the identification of the most frequent items within distributed data. Existing methods for this task often encounter challenges such as c…
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
Shanshan Han, Baturalp Buyukates, Zijian Hu +13
This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defens…
FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training
Zhenheng Tang, Xiaowen Chu, Ryan Yide Ran +7
Federated Learning (FL) enables collaborations among clients for train machine learning models while protecting their data privacy. Existing FL simulation platforms that are design…
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
Weizhao Jin, Yuhang Yao, Shanshan Han +5
Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated…
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain
Baturalp Buyukates, Chaoyang He, Shanshan Han +5
We consider a project (model) owner that would like to train a model by utilizing the local private data and compute power of interested data owners, i.e., trainers. Our goal is to…
Federated Analytics: A survey
Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Shanshan Han +4
Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g.,…