4 papers
DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
Haaris Mehmood, Giorgos Tatsis, Dimitrios Alexopoulos +4
Federated learning enables collaborative model training across distributed clients, yet vanilla FL exposes client updates to the central server. Secure-aggregation schemes protect…
DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models
Haaris Mehmood, Jie Xu, Karthikeyan Saravanan +2
Federated learning (FL) enables the collaborative training of large-scale language models (LLMs) across edge devices while keeping user data on-device. However, FL still exposes se…
Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation
Jie Xu, Haaris Mehmood, Rogier Van Dalen +2
Federated learning (FL) enables training of a global model while keeping raw data on end-devices. Despite this, FL has shown to leak private user information and thus in practice,…
H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning
Shiyuan Zuo, Rongfei Fan, Cheng Zhan +3
Federated Learning (FL) enables decentralized model training without sharing raw data. However, it remains vulnerable to Byzantine attacks, which can compromise the aggregation of…