Federated learning has become a key architecture for training artificial intelligence models without compromising data privacy, especially in environments where multiple agents collaborate from distributed devices. However, when those agents generate heterogeneous, non-independent and identically distributed multimodal flows —as occurs in robotics or wearables— conventional algorithms lose effectiveness. To address this challenge, QFedAgent emerges, a hybrid quantum-classical framework that personalizes federated learning for human activity recognition. Its innovative core lies in a fusion module based on variational quantum circuits, which encodes and intertwines accelerometer and gyroscope signals using only 72 quantum rotational parameters, compared to the 33,000 required by a classical fusion with a multilayer perceptron. This represents a reduction of nearly 97% in total parameters, without sacrificing accuracy: in experiments on the OPPORTUNITY dataset, with non-IID partitions by subject, QFedAgent achieved 97.7% average accuracy. This parametric efficiency not only lightens communication between nodes but also opens the door to artificial intelligence systems for companies that need to deploy lightweight and secure models in resource-constrained environments. At Q2BSTUDIO we understand that integrating quantum techniques with federated learning is a promising frontier, and that is why we offer artificial intelligence solutions for companies that can incorporate these advances into real applications. Our team develops custom applications, from orchestrating AI agents to securing communication channels, leveraging AWS and Azure cloud services to scale reliably. Additionally, we combine business intelligence services such as Power BI to visualize the performance of federated models and make data-driven decisions. The synergy between quantum computing and personalized federated learning is not just theory: at Q2BSTUDIO we work to turn it into custom software that makes a difference in sectors such as healthcare, Industry 4.0, and collaborative robotics.

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