FLAME: A Federated Learning Benchmark for Robotic Manipulation
Authors: name: Santiago Bou Betran, name: Alberta Longhini, name: Miguel Vasco, name: Yuchong Zhang, name: Danica Kragic

Recent progress in robotic manipulation has been fueled by large-scale
datasets collected across diverse environments. Training robotic manipulation
policies on these datasets is traditionally performed in a centralized manner,
raising concerns regarding scalability, adaptability, and data privacy. While
federated learning enables decentralized, privacy-preserving training, its
application to robotic manipulation remains largely unexplored. We introduce
FLAME (Federated Learning Across Manipulation Environments), the first
benchmark designed for federated learning in robotic manipulation. FLAME
consists of: (i) a set of large-scale datasets of over 160,000 expert
demonstrations of multiple manipulation tasks, collected across a wide range of
simulated environments; (ii) a training and evaluation framework for robotic
policy learning in a federated setting. We evaluate standard federated learning
algorithms in FLAME, showing their potential for distributed policy learning
and highlighting key challenges. Our benchmark establishes a foundation for
scalable, adaptive, and privacy-aware robotic learning.