FLamby: Federated learning benchmarks for realistic healthcare settings
A central bottleneck in federated learning research is the lack of realistic, multi-center benchmarks reflecting the heterogeneity of clinical data.
Abstract
Collaborators: EPFL (Switzerland), INRIA (France), University of Southern California (USA), Institut Polytechnique de Paris (France), University of California, Berkeley (USA)
A central bottleneck in federated learning research is the lack of realistic, multi-center benchmarks reflecting the heterogeneity of clinical data. Within this international collaboration, I contributed to the development of FLamby, a benchmark suite comprising seven real-world medical datasets with natural institutional splits across histopathology, radiology, dermatology, and tabular clinical data. Published at NeurIPS 2022 (Datasets and Benchmarks Track), FLamby demonstrated that standard federated learning approaches often underperform pooled training and highlighted the need for heterogeneity-aware optimization methods.
Our role
Contributing collaborator with responsibility for dataset preparation, benchmark design, and evaluation of federated learning methods.
Perspective
FLamby established a widely used community reference for cross-silo federated learning in healthcare. Its open-source release continues to inform benchmarking practices and supports subsequent federated learning studies and methodological developments.
