FedL2G: Learning to Guide Local Training in Heterogeneous Federated Learning

Data and model heterogeneity are two core issues in Heterogeneous Federated Learning (HtFL). In scenarios with heterogeneous model architectures, aggregating model parameters becomes infeasible, leading to the use of prototypes (i.e., class representative feature vectors) for aggregation and guidance. However, they still experience a mismatch between the extra guiding objective and the client's original local objective when aligned with global prototypes. With theoretical guarantees, FedL2G efficiently implements the learning-to-guide process using only first-order derivatives w.r.t. We conduct extensive experiments on two data heterogeneity and six model heterogeneity settings using 14 heterogeneous model architectures (e.g., CNNs and ViTs) to demonstrate FedL2G's superior performance compared to six counterparts. With the rapid development of AI techniques (Touvron et al., 2023; Achiam et al., 2023), public data has been consumed gradually, raising the need to access local data inside devices or institutions (Ye et al., 2024). However, directly using local data often raises privacy concerns (Nguyen et al., 2021). Federated Learning (FL) is a promising privacy-preserving approach that enables collaborative model training across multiple clients (devices or institutions) in a distributed manner without the need to move the actual data outside clients (Kairouz et al., 2019; Li et al., 2020).