FOIN: A Federated Multi-Organoid Intelligence Architecture using Combinatorial Graph Modelling

Organoid Intelligence (OI) is a paradigm for computing which combines biological neural systems with artificial intelligence to enable adaptive and energy-efficient computing. Most of the current organoid-based systems are centralized and noncollaborative and have very limited capabilities for collaborative learning. To overcome these challenges, a hybrid distributed cognition architecture, called a Federated Organoid Intelligence Network (FOIN), is proposed, which integrates brain organoids, federated learning, neuromorphic computing, and edge AI to build a distributed biohybrid cognitive method. The proposed model aims to train multiple brain organoids independently with localized electrical and sensory stimuli using microelectrode array interfaces. Deep learning-based feature extraction module is used to record and analyze neural spike activities and synaptic responses to obtain cognitive embeddings. Each organoid does not send raw biological data to the aggregation server but rather only sends the parameters to the model in an encrypted form, which preserves the biological privacy of each organoid and lowers communication overhead. The global model of intelligence is shared among the distributed nodes of the organoids to learn collectively by adapting. Additionally, the architecture features explainable AI features, secure synchronization using blockchain, and predictive modeling using digital twin to boost transparency, security, and scalability. Experimental and simulation-based evaluations indicate that the proposed framework achieves nearly 96% learning accuracy while reducing computational energy consumption by approximately 50% compared to traditional centralized AI systems. In addition, the federated architecture improves privacy preservation efficiency up to 98% by avoiding direct sharing of raw biological neural data across the network. FOIN has several benefits such as ultra-low-power computing, continuous neuroplastic version, fault tolerance and privacy-preserving shared intelligence. They can be used in applications like cybersecurity anomaly detection, personalized healthcare, cognitive robotics, drug discovery, and even smart edge systems. This work covers a new way in distributed biological computing that connects the fields of artificial intelligence, neuroscience and federated cognitive systems towards next generation sustainable intelligent platforms. The proposed combinatorial node-selection strategy provides a basis for identifying an effective set of organoid nodes that balances cognitive contribution, network connectivity, communication overhead, and energy consumption.