{"repo":"TsingZ0/PFLlib","free":true,"listed":false,"github":"https://github.com/TsingZ0/PFLlib","clone":"git clone https://github.com/TsingZ0/PFLlib.git","description":"Master Federated Learning in 2 Hours—Run It on Your PC!","language":"Python","stars":2153,"topics":["non-iid","federated-learning","personalization","pytorch","python","distributed-computing","heterogeneity","differential-privacy","privacy","imagenet"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"PFLlib: Personalized Federated Learning Library and Benchmark 🎯 We built a beginner-friendly federated learning (FL) library and benchmark: master FL in 2 hours—run it on your PC! Contribute your algorithms, datasets, and metrics to grow the FL community. 👏 The official website and leaderboard is live! Our methods—FedCP, GPFL, and FedDBE—lead the way. Notably, FedDBE stands out with robust performance across varying data heterogeneity levels. Figure 1: An Example for FedAvg. You can create a scenario using generate DATA.py and run an algorithm using main.py , clientNAME.py , and serverNAME.py . For a new algorithm, you only need to add new features in clientNAME.py and serverNAME.py . 🎯 If you find our repository useful, please cite the corresponding paper: Key Features - 39 traditional FL (tFL) and personalized FL (pFL) algorithms, 3 scenarios, and 24 datasets. - Real-machine deployment: HtFL-OnDevice. - Some experimental results are avalible in its paper and here. - Refer to examples to learn how to use it. - Refer to easy to extend to learn how to add new data or algorithms. - The benchmark platform can simulate scenarios using the 4-layer CNN on Cifar100 for 500 clients on one NVIDIA GeForce RTX 3090 GPU card with only 5.08GB GPU memory cost. - We provide privacy evaluation and systematical research support. - You can now train on some clients and evaluate performance on new clients by setting args.num new clients in ./system/main.py . Please note that not all tFL/pFL ","default_branch":null,"files":null,"tree":[],"storefront":"/r/TsingZ0","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/TsingZ0/PFLlib/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}