Open Access

Mirko Konstantin, Stefan Zachow, Anirban Mukhopadhyay

• Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy by keeping data local. A central challenge in FL is determining which client updates are beneficial for aggregation with respect to each client’s target domain. Existing methods typically address this problem in parameter space by comparing model parameters or gradients. However, parameter-space similarity is often a poor proxy for predictive behavior, particularly in heterogeneous settings where client data is not independent and identically distributed. As a result, updates that are misaligned with a client’s target domain, including those arising from heterogeneous data distributions or malfunctioning clients, may be incorporated into aggregation and degrade local model performance. In this work, we propose Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR), a federated learning framework that performs update selection in the function space. Central to our method is an NTK-based agreement score that characterizes predictive behavior and is used to determine the optimal aggregation set for each client. By relating model parameters to local predictive responses, the Neural Tangent Kernel (NTK) enables a function-space characterization of model updates and provides a more expressive criterion for update selection than parameter-space similarity alone. Since access to function-space information before aggregation is not available in conventional centralized FL, LIGHTYEAR leverages a peer-to-peer (P2P) topology, where clients exchange updates directly and can locally evaluate incoming models on private validation data. This enables each client to construct a personalized aggregation set consisting only of updates that are beneficial with respect to its own target domain. The selected updates are then aggregated using a regularized aggregation rule that further stabilizes training under heterogeneity. Our empirical evaluation across five datasets and nine baseline methods demonstrates that LIGHTYEAR consistently outperforms both centralized FL baselines and existing P2P approaches.