Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence (2505.06907v1)

Abstract: The rise of LLMs, such as ChatGPT, DeepSeek, and Grok-3, has reshaped the artificial intelligence landscape. As prominent examples of foundational models (FMs) built on LLMs, these models exhibit remarkable capabilities in generating human-like content, bringing us closer to achieving artificial general intelligence (AGI). However, their large-scale nature, sensitivity to privacy concerns, and substantial computational demands present significant challenges to personalized customization for end users. To bridge this gap, this paper presents the vision of artificial personalized intelligence (API), focusing on adapting these powerful models to meet the specific needs and preferences of users while maintaining privacy and efficiency. Specifically, this paper proposes personalized federated intelligence (PFI), which integrates the privacy-preserving advantages of federated learning (FL) with the zero-shot generalization capabilities of FMs, enabling personalized, efficient, and privacy-protective deployment at the edge. We first review recent advances in both FL and FMs, and discuss the potential of leveraging FMs to enhance federated systems. We then present the key motivations behind realizing PFI and explore promising opportunities in this space, including efficient PFI, trustworthy PFI, and PFI empowered by retrieval-augmented generation (RAG). Finally, we outline key challenges and future research directions for deploying FM-powered FL systems at the edge with improved personalization, computational efficiency, and privacy guarantees. Overall, this survey aims to lay the groundwork for the development of API as a complement to AGI, with a particular focus on PFI as a key enabling technique.

• The paper introduces Personalized Federated Intelligence (PFI) as a framework integrating federated learning with foundation models for personalized AI.
• It surveys recent advances in federated learning and foundation models, exploring how FMs can enhance personalized federated systems.
• PFI addresses key challenges of large foundation models like privacy, efficiency, adaptability, and continuous data for personalization in decentralized settings.

A Survey on Foundation Models for Personalized Federated Intelligence

The landscape of artificial intelligence has undergone substantial evolution with the advent of LLMs like ChatGPT, DeepSeek, and Grok-3. These foundational models demonstrate significant capabilities in generating human-like text, moving closer to realizing artificial general intelligence (AGI). However, their scale poses challenges related to personalization, privacy, and resource demands. This paper by Yu Qiao et al. addresses these challenges, presenting a vision of artificial personalized intelligence (API) that leverages powerful models customized to individual user needs while preserving privacy and computational efficiency.

Key Contributions and Motivations

The authors propose personalized federated intelligence (PFI) as the framework to address the limitations of these LLMs. PFI merges federated learning (FL) with foundation models (FMs), integrating the privacy-preserving benefits of FL with the zero-shot generalization abilities of FMs. The paper highlights several motivations behind PFI:

1. Privacy and Efficiency: The sensitive nature and enormous computational requirements of FMs, such as GPT-3, make them difficult to adapt for specific user needs without compromising privacy or incurring exorbitant costs.
2. Adaptability: The massive scale of these models, while advantageous for general tasks, limits their effectiveness in applications requiring domain-specific customization or personalization for unique data patterns.
3. Continuously Evolving Data: With continual data generation, maintaining and updating large models is challenging and costly, underscoring the necessity for flexible and updatable systems at the edge.

Survey and Review of Related Technologies

The paper reviews recent advances in FL and FMs, exploring how FMs can enhance federated systems. It introduces personalized federated intelligence (PFI), delineating how FL can be deployed with robust FMs for personalized, efficient, and privacy-centered intelligence at the edge. The framework proposes:

• Efficient PFI: Focus on computational resources, communication costs, and personalization strategies.
• Trustworthy PFI: Addressing the reliability and security of federated systems using retrieval-augmented generation (RAG) for better consistency in model outputs.
• Challenges: Key hurdles include effective deployment of FL-powered FMs, model heterogeneity, privacy guarantees, and personalization balance.

Future Directions

The authors suggest promising paths for future research:

1. Meta-PFI: Exploring an interaction ecosystem to enhance autonomy and adaptability within PFI.
2. Quantum-Enabled PFI: Leveraging quantum computing for efficient personalization and privacy, potentially transforming model training and adaptation processes.
3. Sustainable and Green PFI: Developing eco-friendly practices in PFI to reduce carbon footprint and promote green solutions.

Implications and Speculation

The implications of framework such as PFI are profound, merging the robustness and generalization of FMs with the personalization capabilities of FL. Practically, it suggests new architectures for edge deployment where resource limitations and privacy concerns are paramount. Theoretically, it charts a path towards greater synergy between decentralized learning paradigms and powerful AI models, potentially marking a shift in AI research focus from AGI to API.

In conclusion, the paper lays a comprehensive foundation for API development, focusing on PFI as a critical technology in enabling personalized, intelligent services while overcoming obstacles related to scale, privacy, and cost. This fusion of FL with FMs represents a critical step in the evolution of AI systems towards personalized user experiences in decentralized environments.