Optimizing Legal Information Access: Federated Search and RAG for Secure AI-Powered Legal Solutions

Large Language Models (LLMs) have gained increasing importance in the field of Legal Intelligence, enabling the development of applications to assist both legal professionals and ordinary citizens.However, centralized training of these legal LLM models raises concerns about data privacy, as legal data is distributed across various institutions that contain sensitive information about individuals.Using Federated Learning (FL), legal LLMs can be locally trained on devices or clients, and their parameters are aggregated and distributed to a central server, ensuring data privacy without directly sharing the raw data. The presence of private information of the parties in legal data calls for new studies based on legal Artificial Intelligence (legal AI) to investigate new decentralized learning methods that can protect the privacy of individuals.A promising technique in this field is FL, which is useful for enabling multiple participants to collaboratively train a shared model while effectively protecting private sensitive data. However, it has significant limitations, which can be effectively addressed using a technology like FS.FS is a crucial system in managing distributed information in modern environments and it can play a fundamental role in finding relevant information from diverse heterogeneous sources to generate well-informed answers without requiring specific training on data, which proves to be a less flexible solution.FS systems aggregate results from different sources, selecting the most appropriate documents to improve the quality of results and align with the user’s intent. To achieve this goal, new technologies are being utilized, such as Retrieval-Augmented Generation (RAG) in the case study presented.In summary, FL therefore allows training AI models on distributed data without centrally sharing them, ensuring protection of sensitive data and collaboration between organizations without compromising data confidentiality.However, it has important limitations, including computational and communication overhead; in fact, distributed training requires significant computational power, which may not be sustainable for all devices or clients, and it may not be fully efficient, as model optimization in decentralized contexts often doesn’t reach the levels of centralized training, especially for complex models like LLMs. FS is a good technology that addresses these limitations, in particularly specific domains, such as the legal field, by focusing on retrieving information from distributed sources rather than on model training. This approach offers several advantages over FL, first of all lower computational requirements; in fact, since it is not necessary to train models on distributed devices, the resources required are significantly lower and subsequently better results for informative tasks in scenarios where it is necessary to generate answers based on heterogeneous information, allowing to retrieve relevant documents from different sources and better integrate the results to generate coherent and informed answers.Furthermore, the major strength concerns flexibility; FS systems can easily adapt to new data or sources without the need for retraining.This study aims to analyze the limits of FL in the legal field through an excursus on the use of these technologies, supporting the development of new FS methods, especially in the context of RAG pipelines.