arXiv:2511.16992v3 Announce Type: replace Abstract: Aligning Large Language Models (LLMs) with human values often involves balancing multiple, conflicting objectives such as helpfulness and harmlessness. Training these models is computationally intensive, and centralizing the process raises significant data privacy concerns. Federated Learning (FL) offers a compelling alternative, but existing Federated Multi-Objective Optimization (FMOO) methods face severe communication bottlenecks as their reliance on transmitting multiple gradients to a server is unscalable for large models. We introduce F
The increasing scale and complexity of LLMs, coupled with heightened data privacy regulations and the need for more nuanced alignment, are driving innovation in distributed training methods.
Federated learning for LLM alignment addresses critical privacy concerns and computational bottlenecks, potentially enabling broader and more ethical deployment of advanced AI across sensitive domains.
This advancement changes how LLMs can be trained and aligned, moving away from centralized, data-intensive processes towards distributed, privacy-preserving methods, fostering greater accessibility and trust.
- · Healthcare sector
- · Financial institutions
- · AI ethics research
- · Cloud computing providers with FL solutions
- · Centralized LLM development models
- · Organizations with weak data privacy practices
Improved privacy and scalability for LLM development and deployment.
Increased adoption of LLMs in highly regulated and sensitive industries due to enhanced privacy guarantees.
Acceleration of sovereign AI initiatives as nations can develop and align models using decentralized, in-country data without compromising national security or privacy.
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