Large Language Models (LLMs) have significantly propelled the advancement of edge intelligence and have been widely deployed across various scenarios, including autonomous driving, industrial inspection, and personalized IoT services. However, the collaborative adaptation of LLMs on edge devices continues to face formidable challenges due to strict data privacy constraints, highly heterogeneous computing and communication resources, and the non-independent and identically distributed (non-IID) nature of local data. Federated Fine-Tuning (FFT) enables the collaborative optimization of distributed models without exposing raw data. Yet, traditional synchronous aggregation suffers from a severe straggler effect, resulting in high system latency and low resource utilization. Existing asynchronous federated learning methods are predominantly designed for small-to-medium-scale models and struggle to address the specific challenges inherent in LLM fine-tuning namely, model drift caused by stale updates, aggravated client drift stemming from data heterogeneity, and aggregation fairness imbalance resulting from the dominance of fast clients. To address these issues, this paper proposes AlignFed, an asynchronous federated fine-tuning framework for LLMs tailored to heterogeneous edge environments. AlignFed employs a lightweight multi-stage semantic alignment mechanism comprising three core modules: version-aware update grouping, cross-version semantic alignment based on a mini-batch calibration set, and fairness-aware aggregation that integrates both update freshness and client participation frequency. This framework effectively mitigates cross-version model drift and client drift while enhancing aggregation fairness, thereby achieving stable and efficient asynchronous federated optimization in scenarios characterized by high heterogeneity and significant update staleness.

翻译：大语言模型（LLMs）显著推动了边缘智能的发展，已被广泛应用于自动驾驶、工业检测及个性化物联网服务等多种场景。然而，由于数据隐私严格限制、计算与通信资源高度异构，以及本地数据非独立同分布（non-IID）等特性，在边缘设备上协同适配LLMs仍面临严峻挑战。联邦微调（FFT）能够在不暴露原始数据的情况下实现分布式模型的协同优化，但传统同步聚合方法存在严重的掉队者效应，导致系统延迟高、资源利用率低。现有异步联邦学习方法主要用于中小规模模型，难以应对LLM微调所特有的问题：陈旧更新导致的模型漂移、数据异构加剧的客户端漂移，以及快速客户端主导引发的聚合公平性失衡。为解决上述问题，本文提出AlignFed——一种面向异构边缘环境的LLM异步联邦微调框架。AlignFed采用轻量级多阶段语义对齐机制，包含三个核心模块：版本感知的更新分组、基于小批量校准集的跨版本语义对齐，以及融合更新新鲜度与客户端参与频率的公平性感知聚合。该框架有效缓解了跨版本模型漂移与客户端漂移，同时提升了聚合公平性，从而在高异质性及更新显著陈旧场景下实现稳定高效的异步联邦优化。