Coreset-sharing based Collaborative Model Training among Peer Vehicles

Decentralized model training for on-road vehicles offers the potential to harness huge amounts of data at low costs. However, existing approaches usually depend on the existence of a coordinator, tight synchronization, or a connected cluster, all of which can be challenging or infeasible for fast-moving vehicles. In this work, we propose Learning by Chatting (LbChat), a fully decentralized and asynchronous model training approach leveraging coreset-sharing to eliminate the need for a coordinator, tight synchronization, or even a connected cluster. Different from conventional decentralized learning methods, a vehicle not only exchanges its local model but also a coreset, a condensed abstract of its local training data, with opportunistically encountered peers. A vehicle measures its model's performance on a peer's coreset, and a lower performance indicates more different data, thus a more 'valuable' model from the peer. Such models are compressed less during exchange to maximize the aggregate gain from each encounter. Extensive evaluations on the driving decision-making task demonstrate that LbChat is strongly competitive with the central server or roadside infrastructure-based approaches (e.g., federated learning). Compared to recent fully decentralized vehicular learning benchmarks, LbChat out-performs them significantly by up to 20% higher driving success rate in the most challenging driving condition, demonstrating the power of insights gained from coresets on peer models' value.