arXiv:2606.31331v1 Announce Type: new Abstract: Collaborative inference can improve predictive performance by integrating complementary information across agents, but applying collaborative fusion to every sample can incur unnecessary communication and computational overhead. This trade-off is particularly relevant in vertical federated learning (VFL), where clients observe different views of the same sample and fusion typically requires transmitting intermediate representations to a server. We study selective escalation in a two-round VFL inference protocol, in which a low-cost first round pr
The increasing complexity and scale of AI models necessitate more efficient and private multi-party collaboration, driving research into optimized federated learning protocols.
This development improves the efficiency of federated learning, reducing communication and computational overhead, which is crucial for scalable and privacy-preserving AI applications across various industries.
A more adaptable and resource-efficient vertical federated learning inference protocol is introduced, allowing for selective escalation based on predicted gain rather than universal intervention.
- · AI-driven industries handling sensitive data (e.g., healthcare, finance)
- · Cloud computing providers with federated learning services
- · Organizations with distributed data sources
- · Researchers in distributed AI
- · AI systems with high communication overhead
- · Centralized data processing models
- · Inefficient distributed learning approaches
Reduced operational costs and improved privacy in collaborative AI applications will become more feasible.
Broader adoption of federated learning across regulated industries requiring data privacy and distributed analytics will accelerate.
This could contribute to the development of robust, privacy-preserving AI agents operating on diverse and sensitive datasets, impacting 'AI Agents' narratives.
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Read at arXiv cs.LG