arXiv:2409.15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy and governance concerns. Federated Learning offers a decentralized alternative by enabling multiple clients to collaboratively train shared models without exposing raw local data. However, integrating FL with LLMs introduces new challenges, including data heterogeneity, convergence instability, communication overhead, and computational constraints. This survey prov
The increasing performance and widespread adoption of LLMs, coupled with heightened privacy concerns and data governance regulations, are driving the urgent need for decentralized training methods.
This development addresses a critical challenge in scaling LLM deployment while adhering to privacy and data sovereignty, enabling broader, more secure adoption across sensitive applications.
The shift towards federated learning for LLMs allows organizations to leverage powerful AI without centralizing sensitive datasets, fundamentally altering how AI models are built and deployed.
- · Privacy-focused industries (e.g., healthcare, finance)
- · Organizations with proprietary, siloed data
- · Developers of federated learning frameworks
- · Nations prioritizing data sovereignty
- · Centralized data aggregators
- · Cloud providers reliant solely on centralized training
- · AI models requiring massive, unified datasets for training
Companies can deploy more powerful LLMs on internal, sensitive data without compromising privacy or regulatory compliance.
Improved data privacy and localized model training could accelerate AI adoption in highly regulated sectors and potentially foster sovereign AI capabilities.
The development of highly distributed, privacy-preserving AI systems could lead to new forms of collaborative intelligence and a more fragmented, yet robust, global AI landscape.
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