FOIN: A Federated Multi-Organoid Intelligence Architecture using Combinatorial Graph Modelling
Abstract
Abstract 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…
How strong is the evidence?
Evidence Hub reads this as a simulation / modelling, done in cell cultures (in vitro), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.