1 citations · 1 across the 8 of their papers we have counts for
5 papers · 1 filter
Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork
Naoki Nishikawa, Shokichi Takakura, Satoshi Hasegawa
Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD, which repeatedly inject hi…
Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing
Shun Takagi, Satoshi Hasegawa
In cross-device private federated learning, differentially private follow-the-regularized-leader (DP-FTRL) has emerged as a promising privacy-preserving method. However, existing a…
FedDuA: Doubly Adaptive Federated Learning
Shokichi Takakura, Seng Pei Liew, Satoshi Hasegawa
Federated learning is a distributed learning framework where clients collaboratively train a global model without sharing their raw data. FedAvg is a popular algorithm for federate…
Accelerating Differentially Private Federated Learning via Adaptive Extrapolation
Shokichi Takakura, Seng Pei Liew, Satoshi Hasegawa
The federated learning (FL) framework enables multiple clients to collaboratively train machine learning models without sharing their raw data, but it remains vulnerable to privacy…
Privacy Amplification via Shuffled Check-Ins
Seng Pei Liew, Satoshi Hasegawa, Tsubasa Takahashi
We study a protocol for distributed computation called shuffled check-in, which achieves strong privacy guarantees without requiring any further trust assumptions beyond a trusted…