QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition

Quoc Bao Phan; Tuy Tan Nguyen

cs.LGJul 2, 2026

QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition

Authors: Quoc Bao Phan, Tuy Tan Nguyen

Organizations: Department of Electrical and Computer Engineering FAMU-FSU College of Engineering, Florida State University Tallahassee, FL 32310, USA

Abstract

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.

Explore similar work

Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers
May 14, 2026Andras Ferenczi, Sutapa Samanta, Dagen Wang +1Federated Learning Byzantine Attacks
A Drift Stable Quantum Federated Learning for Intelligent Services
Jul 22, 2026Shanika Iroshi Nanayakkara, Shiva Raj PokhrelFederated Learning Quantum Neural Networks
Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction
May 28, 2026Hoang M. Ngo, Quan Nguyen, Wanli Xing +1Federated Learning Federated Averaging

May 14, 2026 · cs.LGJ/K move · Enter open · S save

Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers

Andras Ferenczi, Sutapa Samanta, Dagen Wang, Jason Qizhe Qin

American Express Co · American Express Co. · Columbia University

Federated Learning (FL) trains a global model across decentralized clients while preserving data privacy, but at scale it is vulnerable to malicious updates. Byzantine-resilient aggregation methods such as MultiKrum score gradients against their nearest neighbors and can miss malicious updates that preserve the statistical properties of honest ones. We propose a quantum annealing approach that reformulates client selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem, encoding pairwise distances into a cost function solved by quantum annealers (QA). Unlike MultiKrum's greedy per-client scoring, the QUBO formulation jointly optimizes over all subsets to find the mutually closest group of

m

clients. At small scale (15 clients), QUBO outperforms MultiKrum on the most challenging Byzantine attacks: e.g., Advanced LIE is detected with 95.11% accuracy versus 81.33% on MNIST and 97.78% versus 75.56% on CIFAR-10. QUBO fares poorly on simpler attacks where MultiKrum excels, so the two methods are complementary. QUBO quality also degrades as the number of clients grows. To address this, we introduce a MultiSignal ensemble that uses a dual-feature routing gate based on Euclidean and cosine Krum score gaps to classify attacks into four regimes and routes evasion attacks to a suspicion-penalized QUBO with agreement voting. At 100 clients on MNIST, MultiSignal achieves 95.3% average detection accuracy versus 91.8% for classical MultiKrum, with the largest gains on Sparse Lie (72.0% to 95.2%, +23.2 points) and Advanced Lie (80.4% to 85.2%, +4.8 points). These results show that QUBO-based quantum annealing with MultiSignal is a principled and scalable defense against the most challenging Byzantine strategies in federated learning.

Federated Learning Byzantine Attacks Quantum Approximate Optimization Algorithm Bilevel Optimization

Open paper Ask about it PDF

Abstract

Explore similar work

Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers

A Drift Stable Quantum Federated Learning for Intelligent Services

Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction