This repository implements MocoSFL, a collaborative SSL framework based on Split Federated Learning (SFL). In MocoSFL, the large backbone model is split into a small client-side model and a large server-side model, and only the small client-side model is processed locally on the client's local devices. Together with a smaller batch size enabled by MoCo and SFL's vector concatenation, the FLOPs and memory requirements are reduced by orders of magnitude. Moreover, MocoSFL naturally supports a shared feature memory to enable feature sharing, which provides abundant hard negative keys regardless of the quality and volume of local data. MocoSFL also achieves much better non-IID performance by using a small client-side model and increasing synchronization frequency. On CIFAR-10 with a 2-class-per-client non-IID distribution, we can achieve over ~84% linear probe accuracy with ResNet-18 and 1,000 clients joining simultaneously. The proposed scheme only costs <5MB of memory and <10MB of communication, measured on a Rasiberry Pi 4 device that consumes power less than 5W.
If you plan to use our code in your research, please cite us using:
@misc{li2023mofosfl,
title={MocoSFL: enabling cross-client collaborative self-supervised learning},
author={Jingtao Li and Lingjuan Lyu and Daisuke Iso and Chaitali Chakrabarti and Michael Spranger},
year={2023},
eprint={https://openreview.net/pdf?id=2QGJXyMNoPz},
archivePrefix={openreview}
}
Python > 3.7 with Pytorch, Torchvision
bash scripts/table3/run_sflmoco_cross_client.sh
On Raspberry Pi (configure IP in demo/demo_client_web.py):
cd demo
bash run_client.sh
On PC (configure IP in demo/demo_server_web.py):
cd demo
bash run_server.sh
/run_sflmoco.py -- Source files for MocoSFL
/run_sflmoco_taressfl.py -- Source files for MocoSFL with Target ResSFL module
/expert_target_aware -- Pretrained taressfl checkpoin
/scripts -- Evaluation scripts used on a single-GPU machine
/demo -- Demo scripts for embedded devices
If you have any questions regarding the code, please feel free to contact Jingtao Li (zlijingtao@gmail.com) or Lingjuan Lyu (Lingjuan.Lv@sony.com).