Hierarchical bidirectional aggregation for federated learning under concept drift

Federated Learning (FL) enables collaborative model training across decentralized clients while preserving data privacy. However, real-world FL deployments face two fundamental challenges: heterogeneous data distributions and concept drift, where data distributions evolve over time. While traditional FL assumes a star topology, real-world scenarios often employ hierarchical federated learning based on tree topologies that improve scalability and communication efficiency. In this paper, we introduce Hierarchical Bidirectional Averaging (HBA), a novel aggregation strategy for hierarchical federated networks designed to enhance resilience against data heterogeneity and concept drift. Our main result is that HBA significantly strengthens the resilience of FL against concept drift. In particular, our key findings demonstrate that (1) servers higher in the hierarchy exhibit greater accuracy stability and retain generalized model representations while the lower servers capture more localized and fine-grained details, (2) increasing tree height improves overall client accuracy, and (3) our aggregation strategy enhances generalization and facilitates a balanced information flow across the hierarchy, resulting in faster recovery from concept drift.

Subjects

Bidirectional Aggregation

Concept Drift

Hierarchical Federated Learning

Machine Learning

DDC Class

005.7: Data

Options

Hierarchical bidirectional aggregation for federated learning under concept drift