Paper ID: 2405.15773 • Published Mar 16, 2024
Feature Aggregation with Latent Generative Replay for Federated Continual Learning of Socially Appropriate Robot Behaviours
Nikhil Churamani, Saksham Checker, Fethiye Irmak Dogan, Hao-Tien Lewis Chiang, Hatice Gunes
TL;DR
Get AI-generated summaries with premium
Get AI-generated summaries with premium
It is critical for robots to explore Federated Learning (FL) settings where
several robots, deployed in parallel, can learn independently while also
sharing their learning with each other. This collaborative learning in
real-world environments requires social robots to adapt dynamically to changing
and unpredictable situations and varying task settings. Our work contributes to
addressing these challenges by exploring a simulated living room environment
where robots need to learn the social appropriateness of their actions. First,
we propose Federated Root (FedRoot) averaging, a novel weight aggregation
strategy which disentangles feature learning across clients from individual
task-based learning. Second, to adapt to challenging environments, we extend
FedRoot to Federated Latent Generative Replay (FedLGR), a novel Federated
Continual Learning (FCL) strategy that uses FedRoot-based weight aggregation
and embeds each client with a generator model for pseudo-rehearsal of learnt
feature embeddings to mitigate forgetting in a resource-efficient manner. Our
results show that FedRoot-based methods offer competitive performance while
also resulting in a sizeable reduction in resource consumption (up to 86% for
CPU usage and up to 72% for GPU usage). Additionally, our results demonstrate
that FedRoot-based FCL methods outperform other methods while also offering an
efficient solution (up to 84% CPU and 92% GPU usage reduction), with FedLGR
providing the best results across evaluations.