AttFL: A Personalized Federated Learning Framework for Time-series Mobile and Embedded Sensor Data Processing
These codes demonstrate a proof-of-concept for a novel federated learning technique. To assess the efficacy of the proposed framework, AttFL, we evaluate it on the MNIST and CIFAR-10 datasets using the Bi-LSTM model. Notably, the framework leverages the exchange of small-sized attention modules to achieve high accuracy, low communication and computation costs, and personalized models. The paper describing the proposed framework is currently under review by the Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) journal for publication in 2023.
- PyTorch: 1.10.1+cu111
- Python: 3.8.10
- sklearn: 1.1.1
- torchvision: 0.11.2+cu111
- Numpy: 1.22.0