# AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments

arXiv: https://arxiv.org/abs/2606.08197 · HTML: https://mlsanity.com/paper/2606.08197/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 45 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Builds on (most cited first, 30 of 32)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [FedKD: Communication Efficient Federated Learning via Knowledge Distillation](https://mlsanity.com/paper/2108.13323/) (2021)
- [Improving LoRA in Privacy-preserving Federated Learning](https://mlsanity.com/paper/2403.12313/) (2024)
- [FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning](https://mlsanity.com/paper/2309.00363/) (2023)
- [Selective Aggregation for Low-Rank Adaptation in Federated Learning](https://mlsanity.com/paper/2410.01463/) (2024)
- [On the Origins of Linear Representations in Large Language Models](https://mlsanity.com/paper/2403.03867/) (2024)
- [Large Language Models Empowered Autonomous Edge AI for Connected Intelligence](https://mlsanity.com/paper/2307.02779/) (2023)
- [Fed2: Feature-Aligned Federated Learning](https://mlsanity.com/paper/2111.14248/) (2021)
- [Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity](https://mlsanity.com/paper/2405.16585/) (2024)
- [Federated LoRA with Sparse Communication](https://mlsanity.com/paper/2406.05233/) (2024)
- [LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank Adaptation](https://mlsanity.com/paper/2504.07448/) (2025)
- [Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters](https://mlsanity.com/paper/1912.13075/) (2019)
- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [FADAS: Towards Federated Adaptive Asynchronous Optimization](https://mlsanity.com/paper/2407.18365/) (2024)
- [Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities](https://mlsanity.com/paper/2509.08302/) (2025)
- [Asymmetrically Decentralized Federated Learning](https://mlsanity.com/paper/2310.05093/) (2023)
- [FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting](https://mlsanity.com/paper/2406.02877/) (2024)
- [Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices](https://mlsanity.com/paper/2412.20004/) (2024)
- [SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronization](https://mlsanity.com/paper/2506.09660/) (2025)
- [Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning](https://mlsanity.com/paper/2412.17723/) (2024)
- [Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset](https://mlsanity.com/paper/2508.01675/) (2025)
- [Semantic-aware Wasserstein Policy Regularization for Large Language Model Alignment](https://mlsanity.com/paper/2602.01685/) (2026)
- [Orthogonal Calibration for Asynchronous Federated Learning](https://mlsanity.com/paper/2502.15940/) (2025)
- [Mitigating Participation Imbalance Bias in Asynchronous Federated Learning](https://mlsanity.com/paper/2511.19066/) (2025)

## Abstract (arXiv)

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.