QFedAgent: 量子強化型パーソナライズ連合学習によるマルチエージェント行動認識
QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
量子回路を用いた融合モジュールを組み込んだハイブリッド量子古典連合学習フレームワークを提案し、マルチエージェントの行動認識タスクでパラメータ数を大幅削減しつつ高精度を達成した。
詳しい要約
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著者: Quoc Bao Phan, Tuy Tan Nguyen
分類: cs.LG, cs.AI
原文アブストラクト
Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data, making it suitable for privacy-sensitive robotic sensing applications. However, multi-agent systems generate heterogeneous and non-independent and identically distributed (non-IID) multimodal sensor streams that degrade conventional FL algorithms, while classical fusion modules introduce substantial parameter overhead and communication cost. This paper proposes QFedAgent, a hybrid quantum-classical personalized FL framework for multi-agent activity recognition. The approach integrates a variational quantum circuit fusion module that models accelerometer--gyroscope interactions through quantum state encoding and entanglement, requiring only 72 quantum rotation parameters versus 33K in classical multi-layer perceptron-based fusion, achieving approximately 10x total parameter reduction. Experiments on the OPPORTUNITY dataset under subject-based non-IID partitions demonstrate 97.7% mean test accuracy, confirming that parameter-efficient quantum fusion remains competitive with conventional federated baselines.
