arXiv:2607. 29071v1 Announce Type: cross Abstract: Federated learning of foundation models faces a fundamental resource-asymmetry challenge: the institutions holding the most valuable domain-specific data cannot host billion-parameter models.
FlexP-SFT introduces an aggregation-free framework for personalized split federated fine-tuning of large language models, eliminating the client-side aggregation step that traditionally causes communication bottlenecks and straggler issues. The method employs a layer‑flexible alignment strategy to balance personalization and generalization without global synchronization, and formulates split‑ratio selection as a resource‑aware discrete optimization problem. Experiments demonstrate that FlexP-SFT improves both accuracy and latency compared to baselines, achieving a superior resource‑accuracy trade‑off.
arXiv:2606. 15963v1 Announce Type: cross Abstract: Federated fine-tuning of large language models using parameter-efficient methods such as LoRA enables privacy-preserving adaptation of foundation models.
arXiv:2610.32215v2 Announce Type: cross Abstract: The growing computational power of modern GPU data centers has let a few industrial giants train and deploy large language models (LLMs) on massive a...
arXiv:2610.33475v1 Announce Type: cross Abstract: The widespread deployment of foundational Large Language Models (LLM) across cloud datacenters marks a fundamental paradigm shift from stateless deep...
Optimal Hydra is a transformer‑based heterogeneous federated learning framework designed for fine‑tuning large language models. It creates client‑specific sub‑networks, called Hydra‑nets, by selecting global layers and tailoring attention heads to each client’s local data distribution. Experiments on three datasets demonstrate significant performance gains over baseline methods while reducing local resource demands.
The paper introduces FedTAR, a task-aware federated fine‑tuning approach for Mixture‑of‑Experts (MoE) large language models. FedTAR links local client updates to task preferences using routing outputs and Singular Value Decomposition to extract low‑dimensional task coordinates and update directions. It then aggregates updates within and across task clusters, reconstructing the final update to preserve expert specialization and reduce interference, achieving state‑of‑the‑art performance on four benchmark tasks under non‑IID settings.
arXiv:2607. 19349v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challenge.
arXiv:2608. 15311v1 Announce Type: new Abstract: Federated instruction fine-tuning enables Large Language Models (LLMs) to adapt to decentralized, privacy-sensitive data without requiring data sharing.
arXiv:2507. 10540v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has created a diverse landscape of models, each excelling at different tasks.
FedReason introduces a large language model (LLM) that dynamically selects aggregation rules for federated learning by inspecting client model weights in a sandboxed environment. The LLM evaluates candidate rules on a small proxy dataset and submits a Python program that the server executes until the next call, defaulting to FedAvg if the output is invalid. Experiments on CIFAR-10 and CIFAR-100 with ResNet-18 under various heterogeneity and Byzantine scenarios show FedReason outperforming FedAvg by an average of 1.26 percentage points and matching the best server‑side rule on CIFAR‑100, while incurring modest API costs.
arXiv:2606. 03209v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) in privacy-sensitive and resource-constrained environments remains challenging.