CollectiveFL: Edge-to-Edge Collective Intelligence Transfer in Federated Continual Learning

With the rise of intelligent services on edge devices, the focus of intelligence formation has shifted to the user-side, enabling faster and customized services near the data source, without network overhead or privacy concerns. However, on-device edge intelligence faces some challenges caused by limited data availability and resource constraints in computation and memory. Fortunately, there is more and more intelligence nearby. To effectively harness the potential of widespread edge intelligence, we introduce CollectiveFL, a federated edge intelligence framework that facilitates de-biased, robust edge-to-edge knowledge transfer. By tailoring knowledge sharing for each device based on decision logic similarity, we ensure that edge-side learners specialize in their respective purposes and leverage purpose-specific data. Importantly, to mitigate some possible biases on their own or transferred local data, we delegate knowledge transfer to a set of selected neighboring devices rather than one. By sharing and consolidating collective yet customized intelligence, CollectiveFL establishes collaborative edge-only intelligence without the help of remote servers. Our extensive experimental results on four different model architectures using 13 public datasets have demonstrated that CollectiveFL enhances local learning in 55 out of 63 cases (87.3%) while improving the accuracy of individual tasks by up to 14.1%.