The incoming IoT big data era requires efficient and resource-saving mining of large sets of distributed data. To this end, a viable approach could be to attempt to resort to the synergic exploitation of two emerging paradigms, namely, Deep Neural Networks (DNNs) with early exits and Fog Computing. From an IoT perspective, the most appealing feature of DNNs is its hierarchical layer-wise organization that is exploited for feature learning and/or pattern analysis/classification, potentially matching the hierarchical organization of the underlying Fog-supported execution platform. However, both the training and inference phases of current DNNs are too cumbersome to be executed by IoT devices, and their remote and centralized implementation induces large communication delays and is bandwidth-wasting.
Motivated by these considerations, the two-year DeepFog project aims at developing the integration of the emerging paradigm of the Conditional DNNs (CDNNs) with early exits on the novel Fog Computing (FC) platform that is hierarchically organized in multiple networked tiers. For this purpose, the exploitation of the powerful and recent paradigm of Federated Learning (FL) is also utilized for the distributed and asynchronous Fog-supported training of the underlying CDNN under resource and fairness constraints.
Overall, main goals of the DeepFog project are to:
- formalize the main building blocks and functionalities of the proposed DeepFog technological platform, by designing the most suitable algorithms for the CDNN training;
- design and validate through software simulations a novel asynchronous fast-adaptive FL protocol for the training of CDNNs for Fog-supported IoT stream applications;
- design and validate through extensive numerical tests the performance of the overall resulting optimized DeepFog technological platform, and compare its energy-vs.-complexity-vs.-accuracy performance under some real-world datasets for IoT-oriented real-time big-data applications.