Web-Centric Federated Learning over the Cloud-Edge Continuum Leveraging ONNX and WASM

Federated Learning lays its foundation on the computation and availability of data provided by the increasingly popular Edge devices. By performing on-device training, privacy over the data can be ensured and the computational capacity of the devices is directly exploited. On the other hand, the heterogeneous characteristics of such devices and the difficulty of establishing a proper communication network are often complex obstacles to overcome when implementing Federated Learning solutions. Leveraging ONNX Training Web, WebAssembly, and the Cloud-Edge-Client Continuum concept, we propose FLAT, a system that allows Federated Learning algorithms to run seamlessly on Web browsers, in the form of microservices, without the need for any dependencies and configuration. Through this approach, any device equipped with a browser, even in headless mode, can dynamically join a federated learning cluster in plug-and-play mode by simply connecting to a Web address. The effectiveness of the system is tested with the MNIST and CIFAR10 datasets, using respectively a DNN and a CNN with FedAvg as the aggregation strategy and considering different devices and browsers.