arXiv AI
Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity
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arXiv AI
Jul 15
PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs
arXiv:2607. 12111v1 Announce Type: cross Abstract: Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at network edges.
arXiv Machine Learning
Jul 10
Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems
arXiv:2607. 08013v1 Announce Type: new Abstract: Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while protecting data privacy.
arXiv AI
Aug 18
MoE Router-Guided Clustering for Heterogeneous Federated Instruction Tuning
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 Machine Learning
Aug 4
GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems
arXiv:2603. 18540v2 Announce Type: replace Abstract: The increasing complexity of neural networks poses significant challenges for democratizing federated learning (FL) on resource-constrained edge devices.
arXiv AI
Sep 15
Task-Aware Federated Fine-Tuning for MoE-based Large Language Models
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 Machine Learning
Jul 21
OrderMoE: An expert similarity driven distributed edge MoE inference
arXiv:2607. 17154v1 Announce Type: cross Abstract: Although mixture-of-experts, MoE, models have been increasingly adopted to scale large language models with moderate computation cost, it remains challenging to deploy MoE inference over resource-constrained and bandwidth-limited edge infrastructures.