Foundation Models in AI

• Foundation models are large-scale pre-trained deep learning architectures built on transformer backbones that offer transferability, scalability, and minimal fine-tuning.
• They power a range of applications across natural language, computer vision, time series, and healthcare, with examples like BERT, GPT, and CLIP showcasing cross-modal adaptability.
• Recent innovations emphasize federated learning, privacy-aware training, and modular parameter-efficient fine-tuning to meet practical challenges in resource and data-sensitive environments.

Foundation models (FMs) are large-scale pre-trained deep learning architectures—typically built upon transformer backbones—that leverage massive and diverse datasets to produce highly generalizable and semantically rich representations. These models serve as adaptable underpinnings for a range of downstream tasks across numerous data modalities and domains, including natural language processing, computer vision, time-series analysis, healthcare, geospatial intelligence, and scientific and engineering applications. Distinct from traditional single-task models, FMs are designed for transferability, scalability, and minimal task-specific fine-tuning, enabling robust performance with limited labeled data and underpinning some of the most significant advances in AI. Recent research focuses on architectural innovation, federated and privacy-preserving learning, interpretability, application-specific specialization, and alignment with practical deployment constraints.

1. Architectural Principles and Pretraining Paradigms

Foundation models are typically characterized by their use of deep transformer networks, multi-head self-attention mechanisms, and pre-training on corpora of unprecedented scale. FMs are instantiated as:

• Encoder-only architectures (e.g., BERT-style models) optimized for bidirectional context encoding.
• Decoder-only autoregressive transformers (e.g., GPT-family) suitable for generative tasks.
• Encoder-decoder hybrids (e.g., T5, vision-LLMs like CLIP or Flamingo), facilitating mapping between disparate input and output modalities.

Pretraining objectives vary by modality: masked language modeling in NLP, masked autoencoding or contrastive learning for images and time series, and masked token reconstruction or next-token prediction for sequential data. Pretraining may involve multi-task and multi-modal learning, often utilizing paired or weakly paired data across modalities.

For example, the attention mechanism central to FMs is formalized as:

$$\text{Attention}(Q, K, V) = \text{Softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$

where $Q$, $K$, $V$ denote the query, key, and value matrices, and $d_k$ is their dimensionality—a formulation extensible across text, vision, time series, and beyond (Liang et al., 21 Mar 2024).

2. Transfer, Adaptation, and Specialization Mechanisms

FMs are adapted to specific tasks and domains through several transfer methodologies:

• Full or partial fine-tuning: Updating all or a subset of model parameters on task-specific data.
• Parameter-efficient fine-tuning (PEFT): Methods such as adapters, low-rank adaptation (LoRA), and prompt tuning update only a small subset of parameters or introduce lightweight modules, dramatically reducing compute and communication overhead (Chen et al., 2023, Kang et al., 2023).
• Federated transfer learning (FTL): Distributed adaptation wherein private or sensitive data remains locally, with parameter or representation-level updates exchanged and aggregated under privacy constraints (Kang et al., 2023).

Federated approaches are further refined by novel frameworks such as Hierarchical Federated Foundation Models (HF-FMs), which modularize FMs along modality and task axes to match heterogeneity in practical wireless and edge deployments (Abdisarabshali et al., 3 Sep 2025).

Adaptation methods are supplemented by advanced prompting, knowledge distillation, and multi-agent orchestration—mechanisms critical to efficient specialization for resource-constrained, privacy-sensitive, or safety-critical environments.

3. Integration Across Modalities, Domains, and System Levels

FMs enable unified treatment of heterogeneous data sources—text, images, time series, sensor streams, graphs, and geospatial signals—via multi-modal pretraining and architectural modularization. For instance:

• Vision-language FMs utilize contrastive or joint embedding spaces to relate images and corresponding textual descriptions (e.g., CLIP, Flamingo, MedCLIP) (Rajendran et al., 19 Oct 2025).
• Geospatial FMs integrate optical, SAR, and multispectral imagery with tabular and textual sources for SDG-aligned tasks (Ghamisi et al., 30 May 2025).
• Electric power grid FMs exploit graph neural networks (GNNs) as modality-specific encoders, tailored to grid topologies and sensor data (Hamann et al., 12 Jul 2024).
• Domain-specific biomedical FMs provide generalizable backbones for tasks ranging from segmentation, diagnosis, omics, and graph learning (Khan et al., 15 Jun 2024, Ghamizi et al., 16 Jun 2025).

The semantic communication stack demonstrates vertical integration of FMs at effectiveness, semantic, and physical levels, substantially improving throughput and robustness compared to highly specialized, static architectures (Jiang et al., 2023).

4. Federated, Decentralized, and Privacy-Aware Training

Resource demands and data privacy are persistent challenges for FMs, particularly in scenarios involving sensitive or distributed data (e.g., healthcare, wireless edge devices, or collaborative scientific discovery):

• Federated learning enables decentralized training and adaptation, balancing privacy with global generalization. Hybrid schemes (centralized pretraining + federated fine-tuning) are prevalent (Chen et al., 2023, Chen et al., 2 Sep 2025).
• Efficiency and privacy are enhanced by methods such as secure aggregation, differential privacy (DP-SGD, selective DP), and homomorphic encryption. These enable critical use cases where regulatory compliance, ownership, or adversarial robustness are paramount (Kang et al., 2023).

In harsh wireless environments, federated foundation models employ asynchronous and staleness-aware aggregation, lightweight on-device adaptation, hierarchical and device-to-device communication paradigms, and modular update schedules to address communication, energy, and trust constraints (Chen et al., 2 Sep 2025, Abdisarabshali et al., 3 Sep 2025).

5. Domain-Specific Applications

FMs underpin advances across an array of application sectors:

Domain	Key Applications / Models
Medicine	Clinical NLP, imaging (SAM, MedSAM, CLIP/MedCLIP), omics, graph learning, report generation, multi-modal assistants (Khan et al., 15 Jun 2024, Rajendran et al., 19 Oct 2025, Ghamizi et al., 16 Jun 2025, Ochi et al., 31 Jul 2024)
Finance	Sentiment analysis, market forecasting, multimodal reasoning, financial document parsing (FinLFMs, FinTSFMs, FinVLFMs) (Chen et al., 7 Jul 2025)
Geospatial	Land cover, asset wealth, hazard detection, SDG-aligned analytics (CROMA, GFM-Swin, SpectralGPT) (Ghamisi et al., 30 May 2025)
Power grids	Grid topology modeling, simulation acceleration, multi-modal integration (GridFM) (Hamann et al., 12 Jul 2024)
Cyber-physical	Engineering of embedded and heterogeneous systems using LLMs, vision-LLMs, and digital twins (Lu et al., 6 Apr 2025, Shen et al., 1 May 2025)
Time series	Forecasting, anomaly detection, imputation, multi-modality (TimeGPT, Lag-Llama, UniTS) (Liang et al., 21 Mar 2024, Ren et al., 10 Feb 2025)

Robustness in zero- and few-shot adaptation, broad transfer capability, and interpretability-enhancing mechanisms such as visual explanations in anomaly detection and physiologically-grounded textual outputs, are emphasized (Ren et al., 10 Feb 2025, Rajendran et al., 19 Oct 2025, Han et al., 24 Oct 2024).

6. Interpretability, Theory, and Responsible Deployment

A rigorous understanding of FMs requires interpretable, theoretically grounded frameworks:

• Classical machine learning theory provides generalization error bounds, expressivity analysis via VC-dimension and Rademacher complexity, and gradient-based dynamic behavior models (Fu et al., 15 Oct 2024).
• Limitations of post-hoc explainability are highlighted; the need for in-situ, global interpretability is addressed with resource-light, theory-driven methods.
• Ethical and safety challenges include privacy leakage, social bias, hallucinations, fairness in deployment, and model ownership. Techniques such as differential privacy, adversarial debiasing, continual unlearning, and watermarking are active research domains.
• Responsible FM deployment involves clinician- or expert-in-the-loop systems, human-controllable prompting, transparency in reporting resource/environmental impact, and careful real-world validation (Khan et al., 15 Jun 2024, Ghamisi et al., 30 May 2025).

7. Methodological Innovations and Future Directions

Research momentum points to several emerging themes:

• Modular, compositional, and version-controlled FM engineering, drawing on the analogy to traditional software engineering, including declarative APIs, distributed version control (“Git for models”), and collaborative fine-tuning/merging based on Fisher information (Ran et al., 11 Jul 2024).
• Digital twin representations, as an alternative to tokenization, provide physically grounded, outcome-driven abstractions for continuous, multi-modal, and causally cohesive representations—addressing fundamental limitations of token-based modeling in complex real-world systems (Shen et al., 1 May 2025).
• Multi-modal, multi-task (M3T) architectures—particularly hierarchical federated systems—enable scalable, context-aware learning in heterogeneous wireless edge settings (Abdisarabshali et al., 3 Sep 2025).
• Increasing focus on energy-efficient learning, transparent reporting of carbon footprint, and impact-driven deployment tied to global challenges such as the UN Sustainable Development Goals (Ghamisi et al., 30 May 2025).

A plausible implication is that future FMs will be increasingly characterized by modular architectures, multi-level federation, theory-backed transparency, and robust adaptation mechanisms, supporting real-world deployments that are efficient, explainable, and ethically grounded.