3 papers
cs.LG2026
Differential Privacy as a Perk: Federated Learning over Multiple-Access Fading Channels with a Multi-Antenna Base Station
Hao Liang, Haifeng Wen, Kaishun Wu +1
Federated Learning (FL) is a distributed learning paradigm that preserves privacy by eliminating the need to exchange raw data during training. In its prototypical edge instantiati…
cs.LG2026
An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses
Hao Liang, Wanrong Zhang, Xinlei He +2
Differentially Private Stochastic Gradient Descent (DPSGD) is widely used to protect sensitive data during the training of machine learning models, but its privacy guarantee often…
cs.NE2024
PRF: Parallel Resonate and Fire Neuron for Long Sequence Learning in Spiking Neural Networks
Yulong Huang, Zunchang Liu, Changchun Feng +6
Recently, there is growing demand for effective and efficient long sequence modeling, with State Space Models (SSMs) proving to be effective for long sequence tasks. To further red…