10 citations · 40 across the 30 of their papers we have counts for
4 papers · 1 filter
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…
Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning
Maximilian Egger, Mayank Bakshi, Rawad Bitar
We introduce CyBeR-0, a Byzantine-resilient federated zero-order optimization method that is robust under Byzantine attacks and provides significant savings in uplink and downlink…
Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization
Afonso de Sá Delgado Neto, Maximilian Egger, Mayank Bakshi +1
We introduce CYBER-0, the first zero-order optimization algorithm for memory-and-communication efficient Federated Learning, resilient to Byzantine faults. We show through extensiv…
VALID: a Validated Algorithm for Learning in Decentralized Networks with Possible Adversarial Presence
Mayank Bakshi, Sara Ghasvarianjahromi, Yauhen Yakimenka +3
We introduce the paradigm of validated decentralized learning for undirected networks with heterogeneous data and possible adversarial infiltration. We require (a) convergence to a…