{"ID":2857064,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11585","arxiv_id":"2511.11585","title":"Parameter-Efficient and Personalized Federated Training of Generative Models at the Edge","abstract":"Large generative models (for example, language and diffusion models) enable high-quality text and image synthesis but are hard to train or adapt in cross-device federated settings due to heavy computation and communication and statistical/system heterogeneity. We propose FedGen-Edge, a framework that decouples a frozen, pre-trained global backbone from lightweight client-side adapters and federates only the adapters. Using Low-Rank Adaptation (LoRA) constrains client updates to a compact subspace, which reduces uplink traffic by more than 99 percent versus full-model FedAvg, stabilizes aggregation under non-IID data, and naturally supports personalization because each client can keep a locally tuned adapter. On language modeling (PTB) and image generation (CIFAR-10), FedGen-Edge achieves lower perplexity/FID and faster convergence than strong baselines while retaining a simple FedAvg-style server. A brief ablation shows diminishing returns beyond moderate LoRA rank and a trade-off between local epochs and client drift. FedGen-Edge offers a practical path toward privacy-preserving, resource-aware, and personalized generative AI on heterogeneous edge devices.","short_abstract":"Large generative models (for example, language and diffusion models) enable high-quality text and image synthesis but are hard to train or adapt in cross-device federated settings due to heavy computation and communication and statistical/system heterogeneity. We propose FedGen-Edge, a framework that decouples a frozen...","url_abs":"https://arxiv.org/abs/2511.11585","url_pdf":"https://arxiv.org/pdf/2511.11585v3","authors":"[\"Kabir Khan\",\"Manju Sarkar\",\"Anita Kar\",\"Suresh Ghosh\"]","published":"2025-10-11T09:33:15Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.DC\"]","methods":"[\"Diffusion Model\",\"Language Model\",\"LoRA\"]","has_code":false}