# Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients

arXiv: https://arxiv.org/abs/2410.10200 · HTML: https://mlsanity.com/paper/2410.10200/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [Data-Free Knowledge Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2105.10056/) (2021)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations](https://mlsanity.com/paper/2409.05976/) (2024)
- [Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models](https://mlsanity.com/paper/2401.06432/) (2024)
- [SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models](https://mlsanity.com/paper/2308.06522/) (2023)
- [LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning](https://mlsanity.com/paper/2403.17919/) (2024)
- [Selective Aggregation for Low-Rank Adaptation in Federated Learning](https://mlsanity.com/paper/2410.01463/) (2024)
- [Entropy and mutual information in models of deep neural networks](https://mlsanity.com/paper/1805.09785/) (2018)
- [FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients](https://mlsanity.com/paper/2311.11227/) (2023)
- [SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics](https://mlsanity.com/paper/2305.18513/) (2023)

## Built on by (most cited first, 3 of 3)

- [PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations](https://mlsanity.com/paper/2505.24717/) (2025)
- [Federated Low-Rank Adaptation for Foundation Models: A Survey](https://mlsanity.com/paper/2505.13502/) (2025)
- [PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2411.19335/) (2024)

## Abstract (arXiv)

Federated Learning enables the fine-tuning of foundation models (FMs) across distributed clients for specific tasks; however, its scalability is limited by the heterogeneity of client memory capacities. In this work, we propose Fed-pilot, a memory-efficient federated fine-tuning framework. It enables memory-constrained clients to participate in Low-Rank Adaptation (LoRA)-based fine-tuning by training only a subset of LoRA modules locally. Fed-pilot identifies the optimal selection of trainable LoRA modules as a knapsack optimization problem, maximizing model performance under memory constraints for each client. To mitigate inconsistencies arising from heterogeneous module allocations and Non-IID data, Fed-pilot employs a novel aggregation rule that dynamically compensates for under-updated layers. Extensive experiments on five diverse datasets across various heterogeneous data settings demonstrate Fed-pilot's effectiveness and efficiency compared to state-of-the-art methods. To the best of our knowledge, this is the first study on federated fine-tuning of FMs that integrates memory-constrained optimization. The code will be publicly available.