diwangs/asynchronous-federated-learning

This project is a legacy research artifact, nearly six years old with zero recent activity. While it addresses a legitimate technical hurdle in Federated Learning (FL)—namely, how to handle 'stragglers' and data imbalance through asynchronous updates—it is built on an extremely outdated version of PySyft (likely pre-0.3), which has since undergone massive architectural shifts. In the current competitive landscape, professional-grade frameworks like Flower (flwr.dev), NVIDIA FLARE, and Google's TensorFlow Federated (TFF) have standardized these capabilities with much better production support and security guarantees. With only 63 stars and no velocity, this repo serves as a historical reference rather than a viable tool for modern development. Its defensibility is near zero as the algorithms it implements (like FedAsync variants) are now well-documented in academic literature and available in mature libraries.