Federated Multi-Modal Human Activity Recognition using Multi-Agent Reinforcement Learning

arXiv:2609.33492 · cs.AI · Submitted 2026-09-27 · Read on arXiv

cs.AI

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Accuracy — 66× in this paper · explained in 1 episode
• Aggregation — 40× in this paper · explained in 1 episode
• Reinforcement Learning — 26× in this paper · explained in 4 episodes
• Human Activity Recognition — 23× in this paper
• Federated Learning — 20× in this paper · explained in 15 episodes
• Multimodal Fusion — 15× in this paper · explained in 3 episodes
• Actor — 12× in this paper · explained in 3 episodes
• Regularization — 11× in this paper · explained in 3 episodes
• Local Optimization — 9× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Mixture of Experts — 8× in this paper · explained in 2 episodes
• Proximal Policy Optimization — 6× in this paper
• Deep Reinforcement Learning — 5× in this paper · explained in 2 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Mixture of Experts (MoE) — 4× in this paper · explained in 5 episodes
• Non-IID Data — 4× in this paper · explained in 2 episodes
• Reward Function — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Batch Normalization — 3× in this paper
• Embeddings — 3× in this paper · explained in 2 episodes
• Federated Learning (FL) — 3× in this paper · explained in 7 episodes
• Generalized Advantage Estimation — 3× in this paper
• Robustness — 3× in this paper · explained in 5 episodes
• Adaptive Multimodal Fusion — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Feature Selection — 2× in this paper · explained in 4 episodes
• Multi-Agent Proximal Policy Optimization — 2× in this paper
• Multimodal Dataset — 2× in this paper · explained in 2 episodes
• Proximal Policy Optimization (PPO) — 2× in this paper · explained in 5 episodes
• 1D CNN — 1× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Centralized Training and Decentralized Execution — 1× in this paper
• Computational overhead — 1× in this paper · explained in 2 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Heterogeneity — 1× in this paper · explained in 3 episodes
• Deep Reinforcement Learning (DRL) — 1× in this paper · explained in 2 episodes
• Differentially Private — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Late Fusion — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Model Merging — 1× in this paper · explained in 2 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• MHARFedLLM: Multimodal Human Activity Recognition Using Federated Large Language Model
• GraMFedDHAR: Graph Based Multimodal Differentially Private Federated HAR
• FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
• Proximal Policy Optimization Algorithms
• MEx: Multi-modal Exercises Dataset for Human Activity Recognition

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