Aug 2026· Measurement science and technology· Vol 37, pp. 326113· 0 citations· 40 references
Physics
Abstract
In industrial internet of things (IIoT) deployments, condition monitoring data is rarely complete. Labeled samples are scarce, sensors drop out, signals are corrupted by noise, and what reaches the edge is often a sparse, distorted fragment of the ideal dataset. Federated learning preserves privacy, yet its standard formulations assume clients can train locally on adequate labeled data-an assumption that seldom holds on the factory floor. This paper proposes prompt transfer, a distinct federated learning paradigm that replaces conventional gradient or prototype exchange with the transfer of small, curated sets of labeled examples (prompts) as the sole medium of knowledge flow. Unlike federated distillation, which shares model outputs, or meta-learning, which requires local gradient adaptation, prompt transfer leverages the in-context learning capability of a frozen tabular foundation model to achieve zero-gradient, parameter-free knowledge transfer across clients. Experiments on the Southeast University bearing dataset show that under 5-shot settings, our method FedICL achieves 94.0% accuracy, in contrast to FedAvg’s random-guessing performance of 20.1% while requiring zero gradient computation and reducing communication rounds by 95% and data transfer by 87%. The approach remains effective under non-IID distributions ( 53.9% vs 20% random). This work directly addresses the special issue themes of incomplete data and federated learning, offering a practical solution for label-scarce, resource-constrained IIoT scenarios.
Experimental results show that FedOPAL not only significantly outperforms the original analytical methods on several benchmarks, but also achieves accuracy comparable to state-of-the-art iterative methods while maintaining zero server-side training costs, providing a new engineering paradigm for efficient collaboration of large models on the edge.
Lingyu Qiu, Daniela Annunziata, Stefano Izzo et al.· 2026 2nd International Confe...· 0 citations
One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so without sacrificing model quality is non-trivial, particularly when client data distributions diverge. Recent work has addressed this challenge by aggregating client knowledge on the server through the construction of transferable synthetic datasets or distillates. However, most of these methods lack formal privacy guarantees, leaving a gap in jointly achieving low communication, robustness to heterogeneity, and rigorous privacy. We propose FedKT-CSD (Federated Knowledge Transfer via Collaborative Synthetic Data), a framework inspired by neural image compression that closes this gap by leveraging publicly pretrained autoencoders as a shared latent space. Each client encodes its private data in a single forward pass, computes class-conditional latent statistics, and transmits these to the server. The server aggregates these statistics via secure aggregation, adds calibrated differential privacy noise, and decodes a synthetic dataset for training a global model and further downstream tasks. This design provides formal $(\varepsilon,\delta)$-differential privacy by construction, while keeping client-side computation and communication lightweight. Despite operating under privacy constraints, FedKT-CSD is competitive with and even outperforms non-private baselines across diverse datasets and heterogeneity settings, and scales to a large number of clients. Our code is available at: https://github.com/an7123/FedKT-CSD
Maximilian Andreas Hoefler, Karsten Müller, Wojciech Samek· 0 citations
FedADB, a Class Anchor-Driven Dual-Branch FL framework, a dual-branch collaborative training mechanism designed for clients that achieves significant improvements in both accuracy and convergence speed.
Zhenyan Liu, Hua Zhang, Haoran Gao et al.· 0 citations
A novel FL framework is presented, FedPhoenix, that stochastically re-sets partial parameters in each round to destroy some features of the global model, guiding FL training to learn multiple generalized features for inference rather than specific overfitting features.
Jiahao Wu, Ming Hu, Yanxin Yang et al.· Advances in Neural Informati...· 1 citation
This work proposes RAVEL-FCL, a generative replay-based framework for federated continual learning that integrates an improved generative model based on Rebooting ACGAN with multi-level feature alignment to ensure consistency and employs Elastic Variational Continual Learning on the server to probabilistically regularize the global model and preserve past knowledge.
Yurui Zhou, Jia Hu, Geyong Min et al.· ACM Transactions on Autonomo...· 0 citations
This work proposes a prototype-based, influence-aware federated learning framework (FedProIn) that uses multiple learnable class prototypes to capture shared semantic structures across heterogeneous clients and introduces feature divergence loss and prototype contrastive loss to mitigate client drift by decomposing it into feature drift and prototype drift.