A Deep Dive into Federated Learning of LLMs

Large Language Models (LLMs) such as Llama, GPT, Deepseek, and Mixtral have revolutionized NLP. However, training these models on sensitive/confidential data presents ethical, legal, and infrastructural challenges. Federated Learning (FL) offers a privacy preserving and decentralized alternative. Instead of directly aggregating raw data into a central server, FL coordinates model training across distributed devices, keeping data local and only sharing model updates. This article explores the developing field of federated learning for LLMs, outlining important concepts, practical applications, and comparisons with centralised training methods.

Table of Content

• Federated Learning Fundamentals
• Types of Federated Learning
• FL Frameworks for LLMs
• Industry Use Cases
• Comparison: Federated vs. Centralized Training

Let’s start by understanding the Fundamentals of Federated Learning.

Federated Learning Fundamentals

Federated Learning is a distributed machine learning technique that enables training a global model across multiple decentralized devices or servers without exchanging the data samples themselves. Instead of transferring data to a central server, FL keeps data localized and transfers model updates. This approach offers significant advantages in preserving data privacy, reducing communication costs, and addressing data heterogeneity. The core idea is to train a model collaboratively by aggregating updates from local models trained on individual devices.

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Types of Federated Learning

Federated Learning can be categorized into three main types, based on the data distribution:

Horizontal Federated Learning (HFL)

In HFL, the datasets share the same feature space but differ in samples. For example, different mobile phones have data on the same types of features (e.g., app usage) but for different users.    

Vertical Federated Learning (VFL)

VFL applies to scenarios where datasets share the same sample space but differ in feature space. For instance, different companies having data on the same users but with different attributes (e.g., a bank having financial history, and an e-commerce company having purchase history).    

Federated Transfer Learning (FTL)

This type handles the most general case where datasets differ in both sample and feature space. Transfer learning techniques are used to address the challenges in this scenario.    

Each type of Federated Learning requires specific techniques and algorithms to address the unique challenges posed by the data distribution.

FL Frameworks for LLMs

Flower (FLWR)

Flower, developed by Flower Labs, is a versatile Federated Learning (FL) framework designed for both research and production. It offers broad support for popular machine learning frameworks like PyTorch, TensorFlow, and JAX. Flower’s strength lies in its extensibility, enabling users to customize federated optimization algorithms to suit specific needs.

In the context of Large Language Models (LLMs), Flower has been recently integrated with HuggingFace Transformers. This integration facilitates the fine-tuning of LLMs in a federated manner, across diverse environments such as edge devices or institutional servers. Key features of Flower include support for custom strategies, cross-device and cross-silo FL, and a language-agnostic API.

FedML

FedML, developed by FedML Inc., is an ecosystem focused on scalable, production-ready federated learning. It offers tools for model management and training orchestration, with broad cross-platform support that extends to edge devices.

For Large Language Models, FedML provides open-source templates facilitating federated BERT and GPT training. Notably, “FedLLM” enables collaborative training of domain-specific LLMs across different companies. Key features include ML-Ops for FL, compatibility with IoT/Edge computing, and integration with HuggingFace models.

OpenFL (Open Federated Learning)

OpenFL, developed by Intel, is a framework tailored for secure, decentralized machine learning. It prioritizes applications where data privacy is paramount, notably in healthcare and finance.

In the context of LLM use, OpenFL has been primarily applied to training BERT-style models across distributed settings like hospitals and banks. Key features of OpenFL include secure enclave integration, peer-to-peer orchestration capabilities, and support for Intel’s Software Guard Extensions (SGX).

Industry Use Cases

Healthcare

Federated Clinical BERT: Hospitals use FL to fine-tune LLM models on clinical notes without centralizing sensitive patient data.

Drug Discovery: Collaborative LLM training across pharmaceutical companies accelerates molecule generation while preserving IP.

Finance

Fraud Detection: Banks collaborate using FL-enhanced LLMs to detect fraud patterns across decentralized transaction logs.

Risk Modeling: Institutions co-train LLMs on private datasets for enhanced credit scoring and compliance reporting.

Legal and Government

Legal Document Summarization: FL allows law firms or government bodies to train LLMs without sharing confidential case files.

Federated Policy QA Bots: Ministries use FL-trained LLMs to answer regulatory and policy-related queries, ensuring citizen privacy.

Comparison: Federated vs. Centralized Training

Final Words

Federated learning is now emerging as a powerful enabler of features like privacy-preserving and decentralized training of LLMs. By leveraging latest frameworks like Flower, FedML, and OpenFL, industries ranging from healthcare to finance to Legal can unlock collaborative model training while staying compliant with everchanging data rules. Although FL comes with trade-offs in speed and complexity, its alignment with modern data sovereignty requirements makes it a key component in the future of LLM deployment.

References

• Flower
• FedML
• OpenFL