Publication
Federated Action Recognition for Smart Worker Assistance Using FastPose
Vinit Vikas Hegiste; Vidit Goyal; Tatjana Legler; Martin Ruskowski
In: International Conference on Federated Learning Technologies and Applications. International Conference on Federated Learning Technologies and Applications (FLTA-2025), IEEE, 2025.
Abstract
In smart manufacturing environments, accurate and real-time recognition of worker actions is essential for productivity, safety, and human–machine collaboration. While skeleton based human activity recognition (HAR) offers robustness to lighting, viewpoint, and background variations, most existing approaches rely on centralized datasets, which are impractical in privacy-sensitive industrial scenarios. This paper presents a federated learning (FL) framework for pose-based HAR using a custom skeletal dataset of eight industrially relevant upper-body gestures, captured from five participants and processed using a modified FastPose model. Two temporal backbones, an LSTM and a Transformer encoder, are trained and evaluated under four paradigms: centralized, local (per-client), FL with weighted federated averaging (FedAvg), and federated ensemble learning (FedEnsemble). On the global test set, the FL Transformer improves over centralized training by +12.4 % point, with FedEnsemble delivering a +16.3 % point gain. On an unseen external client, FL and FedEnsemble exceed centralized accuracy by +52.6 and +58.3 % point, respectively. These results demonstrate that FL not only preserves privacy but also substantially enhances cross-user generalization, establishing it as a practical solution for scalable, privacy-aware HAR in heterogeneous industrial settings. Index Terms—Federated learning, smart manufacturing, Industry 4.0, skeleton-based human activity recognition, federated action recognition, federated ensemble learning