Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection

arXiv:2405.01108v1 Announce Type: cross
Abstract: In the pursuit of refining precise perception models for fully autonomous driving, continual online model training becomes essential. Federated Learning (FL) within vehicular networks offers an efficient mechanism for model training while preserving raw sensory data integrity. Yet, FL struggles with non-identically distributed data (e.g., quantity skew), leading to suboptimal convergence rates during model training. In previous work, we introduced FedLA, an innovative Label-Aware aggregation method addressing data heterogeneity in FL for generic scenarios.
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abstract arxiv autonomous autonomous driving continual cs.ai cs.cv cs.lg data data integrity detection distributed distributed data driving federated learning fully autonomous integrity networks object perception raw robust sensory training type while