2 papers
cs.LG2025
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data
Ljubomir Rokvic, Panayiotis Danassis, Boi Faltings
In Federated Learning, heterogeneity in client data distributions often means that a single global model does not have the best performance for individual clients. Consider for exa…
cs.CR2024
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
Ljubomir Rokvic, Panayiotis Danassis, Sai Praneeth Karimireddy +1
In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data. However, traditional data valuation methods are not suitable due to privacy concerns. To a…