mlsanityData to 14 Oct 2026

Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA

arXiv 2409.02346 ↗First posted 4 Sep 2024cs.LG cs.DCcited by 11

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Abstract (arXiv)

Parameter-Efficient Fine-Tuning (PEFT) has risen as an innovative training strategy that updates only a select few model parameters, significantly lowering both computational and memory demands. PEFT also helps to decrease data transfer in federated learning settings, where communication depends on the size of updates. In this work, we explore the constraints of previous studies that integrate a well-known PEFT method named LoRA with federated fine-tuning, then introduce RoLoRA, a robust federated fine-tuning framework that utilizes an alternating minimization approach for LoRA, providing greater robustness against decreasing fine-tuning parameters and increasing data heterogeneity. Our results indicate that RoLoRA not only presents the communication benefits but also substantially enhances the robustness and effectiveness in multiple federated fine-tuning scenarios.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 4 Sep 2024.

Builds on

9 of 17 references.

References · 9

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  2. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  3. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  4. 2021BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelscited by 1,245
  5. 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 689
  6. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  7. 2024LoRA+: Efficient Low Rank Adaptation of Large Modelscited by 377
  8. 2024Improving LoRA in Privacy-preserving Federated Learningcited by 161
  9. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 4.

11 papers.

  1. 2025Robust Federated Finetuning of LLMs via Alternating Optimization of LoRAcited by 17
  2. 2025Decentralized Low-Rank Fine-Tuning of Large Language Modelscited by 14
  3. 2024Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Modelscited by 12
  4. 2025Federated Low-Rank Adaptation for Foundation Models: A Surveycited by 9
  5. 2025Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuningcited by 9
  6. 2026Rethinking LoRA for Privacy-Preserving Federated Learning in Large Modelscited by 5
  7. 2025Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Distributed Fine-Tuningcited by 2
  8. 2025EndoARSS: Adapting Spatially-Aware Foundation Model for Efficient Activity Recognition and Semantic Segmentation in Endoscopic Surgerycited by 1
  9. 2026SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Rankscited by 1
  10. 2026FedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantizationcited by 0
  11. 2026FedLore: Communication and Memory Efficient Federated Learning via Shared Gradient Low-Rank Projectioncited by 0