2 citations · 4 across the 15 of their papers we have counts for
4 papers · 1 filter
AirGC-CD: Gaussian-Circulant Precoding for Exactly Debiasable PAPR Reduction in Over-the-Air Federated Learning
Jonggyu Jang, Hyeonsu Lyu, Hyun Jong Yang
Over-the-air federated learning lets edge devices transmit their local updates simultaneously, reducing the communication overhead. The resulting waveform, however, has a peak-to-a…
Faithful and Fast Influence Function via Advanced Sampling
Jungyeon Koh, Hyeonsu Lyu, Jonggyu Jang +1
How can we explain the influence of training data on black-box models? Influence functions (IFs) offer a post-hoc solution by utilizing gradients and Hessians. However, computing t…
Fed-ZOE: Communication-Efficient Over-the-Air Federated Learning via Zeroth-Order Estimation
Jonggyu Jang, Hyeonsu Lyu, David J. Love +1
As 6G and beyond networks grow increasingly complex and interconnected, federated learning (FL) emerges as an indispensable paradigm for securely and efficiently leveraging decentr…
Deeper Understanding of Black-box Predictions via Generalized Influence Functions
Hyeonsu Lyu, Jonggyu Jang, Sehyun Ryu +1
Influence functions (IFs) elucidate how training data changes model behavior. However, the increasing size and non-convexity in large-scale models make IFs inaccurate. We suspect t…