Adverse drug reactions remain a persistent threat to patient safety, ranging from mild discomfort to life-threatening events. Surveillance data underlines the scale of the problem, with large volumes of serious events reported and substantial mortality recorded alongside the growing complexity of modern pharmacotherapy. At the same time, the most informative clinical data are scattered across institutions, tightly regulated and often unstructured, which frustrates conventional modelling. Federated learning combined with large language models offers a path to analyse distributed text at scale without centralising sensitive records, aligning privacy with performance. A recent scoping review maps how these technologies are being assembled for adverse drug reaction prediction, what datasets and evaluation approaches are in use, and where the field still needs rigour to move from promise to impact.
Why Federated Language Models Matter for ADR Prediction
Unstructured notes, narratives and free-text reports hold much of the clinical signal relevant to adverse reactions, yet their diversity has limited previous approaches. Large language models can capture contextual relationships in such text through transfer learning, while federated learning keeps data local and only shares model updates, reducing privacy risk. Together, federated large language models enable broader participation from sites with restricted data access, expand coverage across diverse sources and remove much of the manual feature engineering burden that traditional pipelines required. The approach scales to multimodal inputs beyond text and can adapt to evolving needs, which is pertinent as pharmacovigilance broadens its evidence base. The review highlights these advantages while noting that documented, real-world use cases remain early, reinforcing the need for systematic development and evaluation.
A foundational challenge is the absence of domain-pretrained open-source language models on adverse reaction corpora due to cost and access barriers. As a result, teams fine-tune general models on domain data for downstream tasks, often pairing encoder architectures with additional layers to support classification targets. Generative models expand possibilities for task orchestration and summarisation of reaction narratives but again depend on careful adaptation to the pharmacovigilance domain. These choices determine not only performance but also transparency and computational feasibility in constrained clinical environments.
Evidence base, data sources and evaluation
The review screened literature from 2019 onwards using Google Scholar and Semantic Scholar, then applied two-stage screening to select studies focused on unstructured data with federated methods and accessible full texts. One hundred and forty-five records met broad criteria, from which twelve were examined in depth to compare tasks, reaction coverage, techniques and reported limitations across informatics and biomedical venues. This mapping underscores that most published applications remain concentrated on structured prediction, with growing but uneven attention to unstructured text workflows.
Multiple benchmark sources support development. These include regulatory surveillance datasets and curated corpora that provide both free-text and coded outcomes, from reaction narratives mapped to standard terminologies to graded severity scales. Such resources enable model training and validation across varied inputs, while acknowledging differences in provenance, reporting incentives and annotation quality. Regulatory programmes in the United States, Europe, Canada and Australia contribute longitudinal incident reports that are particularly valuable for signal detection and severity assessment.
Must Read: Unlocking Drug Discovery with 3D Multi-Omics
Evaluating unstructured predictions requires metrics that reflect semantic fidelity rather than exact lexical overlap. The review describes automated approaches that use language models to score outputs against criteria, n-gram precision and recall measures, embedding-based similarity and edit-distance families, alongside human-in-the-loop validation by clinicians. Each metric offers trade-offs between ease, semantic sensitivity and susceptibility to fluent but incorrect text. Selecting a portfolio of measures and incorporating expert review is therefore integral to credible assessment of model outputs intended to inform clinical judgement.
From model design to deployment: practical strategies and risks
To fit clinical constraints, the review outlines strategies that reduce client-side load during federated fine-tuning, such as higher dropout, low-precision quantisation, parameter segmentation and knowledge distillation that shares only compact student models. These steps enable wider participation by sites with limited computation and can promote fairness across clients with heterogeneous data and infrastructure. Reported experiments show that distilled biomedical encoders can outperform much larger parents on extraction tasks, indicating a viable path to efficient domain performance without prohibitive cost.
Open-source ecosystems provide practical building blocks. Fine-tuning typically uses permissively licensed models, with low-rank adaptation to constrain trainable parameters to under one percent of the base. Frameworks such as TensorFlow Federated, PySyft and other federated runtimes support orchestration, while model merging ranges from simple averaging to more advanced interpolation or pruning of delta weights. Where purpose-built adverse reaction language models are unavailable, teams can fine-tune biomedical embedding models and run retrieval-augmented generation so that a general LLM summarises evidence retrieved from a vector database of domain texts. This indirect route is lighter to train, adaptable to new knowledge and amenable to federated updates on the embedding layer.
Operationalisation is more than an afterthought. Inference commonly requires GPUs and benefits from prompt-engineering patterns embedded in clinician-facing chat interfaces. Hosting can be on-premises or in managed clouds, with pilot deployments gated by access controls and clinical oversight. Two options are described for back ends: direct prompts to a fine-tuned model with lower latency but slower knowledge refresh or retrieval-augmented pipelines that incorporate the most relevant cases at run time. Lightweight web frameworks can accelerate user acceptance testing and continuous delivery into production environments.
Security, interpretability and fairness shape readiness. Post-training vulnerabilities include inversion, adversarial manipulation and data extraction, necessitating defences such as adversarial training, input validation and gradient masking. Attention mechanisms and attribution tools can improve explainability, especially when outputs refer retrieved evidence to make reasoning more transparent. Bias can enter from non-representative client data and skewed sampling; mitigation spans preprocessing, regularisation and fairness-aware optimisation with metrics that monitor group impacts. These concerns come together with limits in data collection control and the non-public nature of many federated environments, which complicate reproducibility. Computation costs and energy use also weigh on design choices and may steer institutions toward cloud inference where utilisation is higher and costs are usage-based.
Federated language models are beginning to bridge the gap between the privacy demands of clinical text and the analytical power required for adverse drug reaction prediction. The evidence base shows a clear rationale, maturing tool chains and credible strategies for efficient fine-tuning, yet also emphasises that real-world deployments remain early and must address evaluation rigour, security, interpretability and fairness. As multimodal capabilities expand and infrastructure becomes more accessible, the approach is well positioned to support safer prescribing and pharmacovigilance workflows, provided teams pair technical advances with robust governance and clinician oversight.
Source: Journal of Medical Internet Research
Image Credit: iStock
