arXiv:2606.28342v1 Announce Type: cross Abstract: Decentralized learning is a promising paradigm for collaborative training in mobile and pervasive systems, as it avoids a central coordinator and does not require sharing raw data. Yet, most analyses rely on idealized communication assumptions that break down in wireless settings, where connectivity is intermittent, topology changes due to mobility, and bandwidth is limited. We study decentralized averaging under client asynchrony, time-varying contact graphs, and technology-dependent throughput constraints. We implement a fully decentralized p
The proliferation of edge devices and increasing demand for localized AI processing necessitates solutions for decentralized learning that account for real-world communication constraints.
This research addresses fundamental communication challenges for decentralized AI, which is critical for scalable and robust AI deployment in mobile and pervasive environments, reducing reliance on centralized cloud infrastructure.
The focus on practical constraints like mobility and bandwidth moves decentralized learning from theoretical idealizations to deployable systems, impacting how AI models are trained and updated at the edge.
- · Edge computing providers
- · Developers of distributed AI applications
- · Telecommunications companies
- · Makers of pervasive devices
- · Centralized cloud AI service providers (in specific use cases)
- · AI models reliant solely on high-bandwidth, stable connections
More resilient and efficient AI applications can be deployed in environments with intermittent connectivity.
This could accelerate the development of truly autonomous edge AI systems that operate independently of constant network access.
It may enable new forms of localized, privacy-preserving AI services across diverse sectors without data leaving the device.
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Read at arXiv cs.AI