Modulated learning for private and distributed regression with just a single sample per client device
arXiv:2605.07233v2 Announce Type: replace Abstract: This work focuses on the question of learning from a large number of devices with each device holding only a single sample of data. Several real-world applications exist to this one sample per client setup up including learning from fitness trackers, data/app usage aggregators, body-worn sensing devices, and daily event monitors to name a few. When a client has only one sample, the standard federated learning paradigm breaks down as a local update based on that single point is far from being useful, especially in the earlier rounds for estima
The proliferation of edge devices with limited individual data points necessitates new privacy-preserving learning paradigms that overcome the limitations of traditional federated learning.
This research addresses a critical challenge in distributed AI, enabling learning from vast, disparate single-sample data sources while maintaining privacy and efficiency, which could unlock new applications.
Current federated learning models are inefficient for single-sample clients; new 'modulated learning' techniques are being developed to make such scenarios viable and scalable.
- · Edge device manufacturers
- · Privacy-preserving AI developers
- · Healthcare and fitness tracking industries
- · AI-powered IoT applications
- · Traditional federated learning frameworks (in single-sample contexts)
- · Centralized data aggregation models
Improved privacy and efficiency for AI models trained on distributed single-sample data.
Expansion of AI applications into highly distributed, sensitive data environments like personal health and IoT.
Potential for new ethical and regulatory challenges regarding the aggregation and use of even single-sample personal data.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG