Feature Aggregation with Latent Generative Replay for Federated Continual Learning of Socially Appropriate Robot Behaviours

2024-03-16

arXiv.org

AI Summary

To address the challenges of dynamically adapting socially appropriate behaviors and mitigating catastrophic forgetting in multi-robot federated continual learning, this paper proposes FedRoot and FedLGR—a synergistic framework. FedRoot decouples feature and task learning to enable lightweight weight aggregation, reducing CPU and GPU overhead by 86% and 72%, respectively. FedLGR introduces a novel latent-space generative replay and pseudo-feature reenactment mechanism, alleviating forgetting without storing raw data. Evaluated in a simulated living-room environment, the framework enables distributed robots to progressively and resource-efficiently learn social norms. Experiments demonstrate consistent superiority over baselines on social behavior discrimination tasks, achieving 84% and 92% reductions in computational and memory resource consumption, respectively. The framework significantly enhances the practicality and scalability of federated continual learning for real-world social robotics applications.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Life-Long and Continual Learning
• Multiagent Systems: Multiagent Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

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.

Problem

• Federated Learning for robot behavior
• Adaptation to dynamic social environments
• Resource-efficient continual learning strategies

Innovation

• Federated Root averaging
• Latent Generative Replay
• Resource-efficient continual learning