LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning
Using a Lazy Influence Approximation

LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation

23 May 2022

Ljubomir Rokvic

Panayiotis Danassis

Sai Praneeth Karimireddy

Boi Faltings

Papers citing "LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation"

6 / 6 papers shown

Title
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data Ljubomir Rokvic Panayiotis Danassis Boi Faltings FedML 31 0 0 05 May 2025
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 144 347 0 25 Sep 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 173 410 0 14 Jul 2021
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 178 154 0 26 Feb 2021
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping Xiaoyu Cao Minghong Fang Jia Liu Neil Zhenqiang Gong FedML 102 610 0 27 Dec 2020
FedML: A Research Library and Benchmark for Federated Machine Learning Chaoyang He Songze Li Jinhyun So Xiao Zeng Mi Zhang ... Yang Liu Ramesh Raskar Qiang Yang M. Annavaram Salman Avestimehr FedML 162 563 0 27 Jul 2020