This repository is the official Pytorch implementation DEMO of FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence. IEEE Transactions on Mobile Computing (TMC). 2024
[Feb. 2024] FedCache is featured by Phoenix Tech. Access here.
[Feb. 2024] Our FedCache is accepted by IEEE Transactions on Mobile Computing (TMC). Access here.
[Jan. 2024] One follow-up paper examines the impact of logits poisoning attack on FedCache. Access here.
[Dec. 2023] We discover a Chinese blog that interprets FedCache on CSDN. Access here.
[Dec. 2023] One follow-up paper confirms the further potential of FedCache for enhanced communication efficiency by accumulating local updates. Access here.
[Aug. 2023] Our FedCache is released on arxiv. Access here.
- FedCache is a device friendly, scalable and effective personalized federated learning architecture tailored for edge computing.
- FedCache guarantees satisfactory performance while conforming to multiple personalized devices-side limitations.
- FedCache improves communication efficiency by up to x200 over previous architectures and can accommodate heterogeneous devices and asynchronous interactions among devices and the server.
If you have any ideas or questions regarding to FedCache, please feel free to contact wuzhiyuan22s@ict.ac.cn.
- Python: 3.10
- Pytorch: 1.13.1
- torchvision: 0.14.1
- hnswlib
- Other dependencies
python main_fedcache.py
Method | MAUA (%) | Communication Cost (G) | Speed-up Ratio |
---|---|---|---|
pFedMe | 94.89 | 13.25 | ×1.0 |
MTFL | 95.59 | 7.77 | ×1.7 |
FedDKC | 89.62 | 9.13 | ×1.5 |
FedICT | 84.62 | - | - |
FD | 84.19 | - | - |
FedCache | 87.77 | 0.99 | ×13.4 |
Method | MAUA (%) | Communication Cost (G) | Speed-up Ratio |
---|---|---|---|
pFedMe | 81.57 | 20.71 | ×1.0 |
MTFL | 83.92 | 12.33 | ×1.7 |
FedDKC | 78.24 | 8.43 | ×2.5 |
FedICT | 76.90 | 13.34 | ×1.6 |
FD | 76.32 | - | - |
FedCache | 77.71 | 0.08 | ×258.9 |
Method | MAUA (%) | Communication Cost (G) | Speed-up Ratio |
---|---|---|---|
pFedMe | 37.49 | - | - |
MTFL | 43.43 | 52.99 | ×1.0 |
FedDKC | 45.87 | 11.46 | ×4.6 |
FedICT | 43.61 | 10.69 | ×5.0 |
FD | 42.77 | - | - |
FedCache | 44.42 | 0.19 | ×278.9 |
Method | MAUA (%) | Communication Cost (G) | Speed-up Ratio |
---|---|---|---|
pFedMe | 31.65 | - | - |
MTFL | 34.09 | - | - |
FedDKC | 43.95 | 4.12 | ×1.3 |
FedICT | 42.79 | 5.50 | ×1.0 |
FD | 39.36 | - | - |
FedCache | 40.45 | 0.07 | ×78.6 |
Method | MAUA (%) | Communication Cost (G) | Speed-up Ratio |
---|---|---|---|
FedDKC | 85.38 | 10.53 | ×1.0 |
FedICT | 80.53 | - | - |
FD | 79.90 | - | - |
FedCache | 83.94 | 0.10 | ×105.3 |
Method | MAUA (%) | Communication Cost (G) | Speed-up Ratio |
---|---|---|---|
FedDKC | 77.96 | 12.64 | ×1.0 |
FedICT | 76.11 | - | - |
FD | 75.57 | - | - |
FedCache | 77.26 | 0.08 | ×158.0 |
Method | MAUA (%) | Communication Cost (G) | Speed-up Ratio |
---|---|---|---|
FedDKC | 44.53 | 4.58 | ×1.2 |
FedICT | 43.96 | 5.35 | ×1.0 |
FD | 40.40 | - | - |
FedCache | 41.59 | 0.05 | ×107.0 |
Method | MAUA (%) | Communication Cost (G) | Speed-up Ratio |
---|---|---|---|
FedDKC | 44.80 | 4.12 | ×1.3 |
FedICT | 43.40 | 5.50 | ×1.0 |
FD | 40.76 | - | - |
FedCache | 41.71 | 0.07 | ×78.6 |
@ARTICLE{wu2024fedcache,
author={Wu, Zhiyuan and Sun, Sheng and Wang, Yuwei and Liu, Min and Xu, Ke and Wang, Wen and Jiang, Xuefeng and Gao, Bo and Lu, Jinda},
journal={IEEE Transactions on Mobile Computing},
title={FedCache: A Knowledge Cache-Driven Federated Learning Architecture for Personalized Edge Intelligence},
year={2024},
volume={},
number={},
pages={1-15},
keywords={Computer architecture;Training;Servers;Computational modeling;Data models;Adaptation models;Performance evaluation;Communication efficiency;distributed architecture;edge computing;knowledge distillation;personalized federated learning},
doi={10.1109/TMC.2024.3361876}
}
FedICT: Federated Multi-task Distillation for Multi-access Edge Computing. IEEE Transactions on Parallel and Distributed Systems (TPDS). 2023
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration. IEEE International Conference on Computer Communications (INFOCOM). 2024
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation. ACM Transactions on Intelligent Systems and Technology (TIST). 2024
Federated Class-Incremental Learning with New-Class Augmented Self-Distillation. arXiv preprint arXiv:2401.00622. 2024
Survey of Knowledge Distillation in Federated Edge Learning. arXiv preprint arXiv:2301.05849. 2023