One of the fundamental issues with federated learning (FL) is the assumption that geographically distributed clients belong to a large single cluster and, therefore, can contribute to a generalized global model. However, this is not the case in most ...

Smart Environmental Monitoring is crucial for public health as it enables us to monitor and battle environmental hazards. We utilize UAVs to form a FANET, which can provide effective communication infrastructure suitable for environmental monitoring....

Federated learning enables the creation of a centralized global model by aggregating updates from the locally trained models from multiple clients. While powerful, such an architecture is limited to applications where the needs of heterogeneous clien...

Training machine learning models typically necessitates extensive training data. Yet, the collection and transmission of huge amounts of data to a central server pose significant challenges, requiring substantial network and storage resources. For mo...

We are going to highlight some of the pressing issues concerning privacy and latency requirements of mission-critical applications and how can we leverage edge AI to mitigate challenges associated with traditional centralized machine learning.