77 citations · 179 across the 54 of their papers we have counts for
14 papers · 1 filter
RW-LoRA: Communication-Efficient Decentralized LoRA Fine-Tuning via Random Walks
Xingran Chen, Rohit Bhagat, Ghadir Ayache +3
Parameter-efficient fine-tuning methods such as LoRA have become a standard approach for adapting large foundation models. Adopting fine-tuning to distributed settings faces severa…
SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks
Yue Xia, Tayyebeh Jahani-Nezhad, Mayank Bakshi +1
We consider federated parameter efficient fine-tuning of large neural networks with low-rank adaptation (LoRA,~Hu et al.\ 2022). Combining LoRA with federated PEFT introduces chall…
ProDiGy: Proximity- and Dissimilarity-Based Byzantine-Robust Federated Learning
Sena Ergisi, Luis Maßny, Rawad Bitar
Federated Learning (FL) emerged as a widely studied paradigm for distributed learning. Despite its many advantages, FL remains vulnerable to adversarial attacks, especially under d…
Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learning
Yue Xia, Tayyebeh Jahani-Nezhad, Rawad Bitar
We propose Fed-DPRoC, a novel federated learning framework designed to jointly provide differential privacy (DP), Byzantine robustness, and communication efficiency. Central to our…
Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning
Yue Xia, Christoph Hofmeister, Maximilian Egger +1
Federated learning (FL) shows great promise in large-scale machine learning but introduces new privacy and security challenges. We propose ByITFL and LoByITFL, two novel FL schemes…
Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning
Maximilian Egger, Rawad Bitar
Ensuring resilience to Byzantine clients while maintaining the privacy of the clients' data is a fundamental challenge in federated learning (FL). When the clients' data is homogen…