Fairness and Accuracy in Federated Learning

Fairness and Accuracy in Federated Learning

18 December 2020

Tianrui Li

Papers citing "Fairness and Accuracy in Federated Learning"

8 / 8 papers shown

Title
Oh the Prices You'll See: Designing a Fair Exchange System to Mitigate Personalized Pricing Aditya Karan Naina Balepur Hari Sundaram 13 0 0 04 Sep 2024
Fairness and Privacy in Federated Learning and Their Implications in Healthcare Navya Annapareddy Jade F. Preston Judy Fox FedML 16 3 0 15 Aug 2023
Heterogeneous Federated Learning: State-of-the-art and Research Challenges Mang Ye Xiuwen Fang Bo Du PongChi Yuen Dacheng Tao FedML AAML 29 244 0 20 Jul 2023
CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization Nang Hung Nguyen Duc Long Nguyen Trong Bang Nguyen T. Nguyen H. Pham Truong Thao Nguyen Phi Le Nguyen FedML 19 8 0 21 Feb 2023
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning Nang Hung Nguyen Phi Le Nguyen D. Nguyen Trung Thanh Nguyen Thuy-Dung Nguyen H. Pham Truong Thao Nguyen FedML 46 24 0 04 Aug 2022
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning Xubo Yue Maher Nouiehed Raed Al Kontar FedML 20 37 0 05 Aug 2021
FedJAX: Federated learning simulation with JAX Jae Hun Ro A. Suresh Ke Wu FedML 27 48 0 04 Aug 2021
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 243 11,659 0 09 Mar 2017