Privacy-Aligned Personalized Federated Learning with Compact Adaptation and Variable-Length Gaussian Communication
Paper recorded by Signals 4 on 2026-09-14 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-14 on arXiv · recorded by Signals 4 on 2026-09-15
Category: cs.LG · 机器学习 · first seen 2026-09-15
Abstract
Record-level differential privacy exposes a structural misalignment in personalized federated learning when client-specific variation is low-dimensional while training repeatedly releases high-dimensional updates. In this paper, we address this misalignment by releasing a private client context once and confining repeated adaptation to a fixed coefficient space. Beyond dimensionality reduction, th
Read on arXiv →
Cite this page: Privacy-Aligned Personalized Federated Learning with Compact Adaptation and Variable-Length Gaussian Communication: the #48 most recent of 215 cs.LG papers we have recorded (as of 2026-09-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/privacy-aligned-personalized-federated-learning-with-compact-adaptation-and-vari.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable:
papers.json
Get 4 AI signals a day by email — free.
Get 4 AI signals a day by email — free