Page 61 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

• Industry, Innovation, and Infrastructure – By fostering innovative, privacy-preserving AI
methods that can be deployed across sectors.
• Reduced Inequalities – By providing a robust federated learning platform that allows
institutions from different regions and resource levels to collaborate without centralizing
their data, CAFEIN helps bridge the digital divide and ensures equitable access to 4.1-Healthcare
advanced AI technologies.
• Partnerships for the Goals – By enabling collaborative efforts between research institutions,
industry, and government bodies without compromising data security.

2�3 Future Work

• Ongoing EU Projects: Continue the deployment and integration of CAFEIN within
ongoing EU projects to validate and refine its use in real-world scenarios.
• Expanded Collaborations: Strengthen and extend collaborations with key international
organizations such as the World Health Organization (WHO) and the World Food
Programme (WFP) to explore applications in public health and humanitarian initiatives.
• Federated Inference Deployment: Enhance the platform by deploying federated
inference capabilities, enabling decentralized, real-time model predictions while ensuring
data privacy remains uncompromised.
• Simplified Deployment: Focus on making the deployment process easier and more
efficient for partner institutions, reducing technical barriers to adoption.
• Wider Deployment: Broaden the scope of deployment by targeting additional industry
sectors and international research collaborations.
• Collaborative Research: Foster further collaborative research initiatives with academic
and industry partners to innovate and optimize federated learning and inference
techniques, ensuring CAFEIN remains at the forefront of privacy-preserving decentralized
AI technology.

3 Use Case Requirements

• REQ_01: It is critical that the platform enable collaborative AI model training and analytics
on distributed datasets without requiring the centralisation of raw data from participating
institutions.
• REQ_02: It is critical that the platform ensure that sensitive data remains localized at each
participating organization, preserving data privacy, supporting regulatory compliance
(e.g., GDPR), and maintaining data sovereignty.
• REQ_03: It is critical that the platform is data-type agnostic, supporting structured and
unstructured data, including numerical, text, and image data, to accommodate diverse
research and industry domains.
• REQ_04: It is critical that the system provide capabilities for federated inference, allowing
AI models to make predictions on decentralized data without transferring the raw input
data to a central server.
• REQ_05: It is critical that the platform provides capabilities for federated inference,
allowing AI models to make predictions on decentralised data without transferring the
raw input data to a central server.
• REQ_06: It is critical that the platform implements comprehensive access controls, data
encryption mechanisms (at-rest and in-transit for model updates), and tamper-evident
audit logs to ensure system security and allow participants to verify against data leakage.

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