Federated Learning of Socially Appropriate Agent Behaviours in Simulated
  Home Environments
Saksham Checker Nikhil Churamani Hatice Gunes
As social robots become increasingly integrated into daily life, ensuring
their behaviours align with social norms is crucial. For their widespread
open-world application, it is important to explore Federated Learning (FL)
settings where individual robots can learn about their unique environments
while also learning from each others' experiences. In this paper, we present a
novel FL benchmark that evaluates different strategies, using multi-label
regression objectives, where each client individually learns to predict the
social appropriateness of different robot actions while also sharing their
learning with others. Furthermore, splitting the training data by different
contexts such that each client incrementally learns across contexts, we present
a novel Federated Continual Learning (FCL) benchmark that adapts FL-based
methods to use state-of-the-art Continual Learning (CL) methods to continually
learn socially appropriate agent behaviours under different contextual
settings. Federated Averaging (FedAvg) of weights emerges as a robust FL
strategy while rehearsal-based FCL enables incrementally learning the social
appropriateness of robot actions, across contextual splits.