Increasing privacy concerns and unrestricted access to data lead to the development of a novel machine learning paradigm called Federated Learning (FL). FL borrows many of the ideas from distributed machine learning, however, the challenges associated with federated learning makes it an interesting engineering problem since the models are trained on edge devices. It was introduced in 2016 by Google, and since then active research is being carried out in different areas within FL such as federated optimization algorithms, model and update compression, differential privacy, robustness, and attacks, federated GANs and privacy preserved personalization. There are many open challenges in the development of such federated machine learning systems and this project will be focusing on the communication bottleneck and data Non IID-ness, and its effect on the performance of the models. These issues are characterized on a baseline model, model performance is evaluated, and discussions are made to overcome these issues.
Various AI search algorithms and deterministic algorithm were evaluvated on their performance for optimal routing of emergency vehicles (Firetrucks and Ambulances in North York, Toronto). Hotspots were identified by using KMeans as a preprocessing step on the historical traffic data of North York Region. Live traffic incidents data from the Microsoft BING API was then used to weigh paths and route the emergency vehicles optimally. Cases of Emergency Responders Accidents and Accidents Emergency Responders were considered into the mapping logic.