Federated Object Detection (FOD) has become increasingly important for developing collaborative models that protect data privacy. However, challenges such as varying environmental conditions and hardware constraints have limited its effectiveness. The proposed FEDEXCHANGE framework addresses these limitations by implementing a server-side dynamic model exchange strategy, which allows clients to benefit from a diverse range of domain data without direct sharing of their local datasets.
By alternating between model aggregation and model exchange, FEDEXCHANGE enables improved learning from different domains while maintaining low computational requirements. This approach has shown to enhance object detection performance significantly, achieving a notable increase in mean average precision in adverse conditions. As the effectiveness of federated learning hinges on the ability to generalize across domains, solutions like FEDEXCHANGE hold the potential to advance the field by making federated learning more applicable to real-world edge scenarios.
👉 Pročitaj original: arXiv AI Papers
