FL-CorePaperList

Overview

• Survey Papers
• Research Papers
• Experimental Papers
• Library

Survey Papers

1. Yang Q, Liu Y, Chen T, et al. Federated machine learning: Concept and applications[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2019, 10(2): 1-19. paper
2. Kairouz P, McMahan H B, Avent B, et al. Advances and open problems in federated learning[J]. Foundations and Trends® in Machine Learning, 2021, 14(1–2): 1-210. paper
3. Tan A Z, Yu H, Cui L, et al. Towards personalized federated learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022. paper

Research Papers

General Federated Learning

1. McMahan B, Moore E, Ramage D, et al. Communication-efficient learning of deep networks from decentralized data[C]//Artificial intelligence and statistics. PMLR, 2017: 1273-1282. paper
2. Konečný J, McMahan H B, Yu F X, et al. Federated learning: Strategies for improving communication efficiency[J]. arXiv preprint arXiv:1610.05492, 2016. paper
3. Li T, Sahu A K, Zaheer M, et al. Federated optimization in heterogeneous networks[J]. Proceedings of Machine learning and systems, 2020, 2: 429-450. paper
4. Karimireddy S P, Kale S, Mohri M, et al. Scaffold: Stochastic controlled averaging for federated learning[C]//International conference on machine learning. PMLR, 2020: 5132-5143. paper
5. Zhao Y, Li M, Lai L, et al. Federated learning with non-iid data[J]. arXiv preprint arXiv:1806.00582, 2018. paper

Personalized Federated Learning

1. Li T, Hu S, Beirami A, et al. Ditto: Fair and robust federated learning through personalization[C]//International Conference on Machine Learning. PMLR, 2021: 6357-6368. paper
2. Collins L, Hassani H, Mokhtari A, et al. Exploiting shared representations for personalized federated learning[C]//International conference on machine learning. PMLR, 2021: 2089-2099. paper
3. Deng Y, Kamani M M, Mahdavi M. Adaptive personalized federated learning[J]. arXiv preprint arXiv:2003.13461, 2020. paper

Async Federated Learning

1. Xie C, Koyejo S, Gupta I. Asynchronous federated optimization[J]. arXiv preprint arXiv:1903.03934, 2019. paper
2. Wu W, He L, Lin W, et al. SAFA: A semi-asynchronous protocol for fast federated learning with low overhead[J]. IEEE Transactions on Computers, 2020, 70(5): 655-668. paper

Experimental Papers

1. Li Q, Diao Y, Chen Q, et al. Federated learning on non-iid data silos: An experimental study[C]//2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2022: 965-978. paper
2. Hsu T M H, Qi H, Brown M. Measuring the effects of non-identical data distribution for federated visual classification[J]. arXiv preprint arXiv:1909.06335, 2019. paper

Library

1. Beutel D J, Topal T, Mathur A, et al. Flower: A friendly federated learning research framework[J]. arXiv preprint arXiv:2007.14390, 2020. paper

2. He C, Li S, So J, et al. Fedml: A research library and benchmark for federated machine learning[J]. arXiv preprint arXiv:2007.13518, 2020. paper