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
[Apr. 2024] FedCache's remote information retrieval has been effectively advanced and implemented by PTaaS. Privacy-Preserving Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems (arxiv.org).
[Mar. 2024] FedCache is featured by Tencent. 机器人再度大幅进化!阿西莫夫三法则还有效吗?(Robots are Evolving Dramatically Again! Is Asimov's "Three Laws of Robotics" Still Valid?).
[Mar. 2024] I was invited to give a talk for Network System and Machine Learning Group, School of Computer Science, Peking University. 面向个性化边缘智能的缓存驱动联邦学习: 研究进展与开放性问题 (Cache-driven Federated Learning for Personalized Edge Intelligence: Research Progress and Open Problems).
[Mar. 2024] FedCache is featured by NGUI. 缓存驱动联邦学习架构赋能个性化边缘智能 (Cache-Driven Federated Learning Architecture Energizes Personalized Edge Intelligence).
[Mar. 2024] FedCache is included by the first survey investigating the application of knowledge distillation in federated edge learning. Knowledge Distillation in Federated Edge Learning: A Survey (arxiv.org).
[Feb. 2024] FedCache is featured on Phoenix Tech. 缓存驱动联邦学习架构来了!专为个性化边缘智能打造 (The Cache-Driven Federated Learning Architecture is Coming! Built for Personalized Edge Intelligence).
[Feb. 2024] FedCache is accepted by IEEE Transactions on Mobile Computing (TMC). FedCache: A Knowledge Cache-Driven Federated Learning Architecture for Personalized Edge Intelligence | IEEE Journals & Magazine | IEEE Xplore
[Jan. 2024] One follow-up paper examines the impact of logits poisoning attack on FedCache. Logits Poisoning Attack in Federated Distillation (arxiv.org).
[Dec. 2023] We discover a Chinese blog that interprets FedCache on CSDN. 缓存驱动的联邦学习架构FedCache (FedCache: Cache-Driven Federated Learning Architecture).
[Dec. 2023] One follow-up paper confirms the further potential of FedCache for enhanced communication efficiency by accumulating local updates. Improving Communication Efficiency of Federated Distillation via Accumulating Local Updates (arxiv.org).
[Aug. 2023] FedCache is featured by Netease. AI在量子计算中的研究进展 (Research Progress of AI in Quantum Computing).
[Aug. 2023] FedCache is released on arxiv. FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence (arxiv.org).
- 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 [email protected].
- 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