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Federated Machine Learning

Definition ∞ Federated Machine Learning is a decentralized approach to artificial intelligence training where multiple entities collaboratively train a shared model. Individual data sets remain localized, never leaving their owners’ devices or servers. Only model updates or parameters are exchanged, preserving data privacy and reducing reliance on centralized data storage. This method allows for the creation of robust models from diverse data sources without direct data sharing.
Context ∞ The current state of Federated Machine Learning shows increasing application in privacy-sensitive domains like healthcare and finance. A key discussion centers on optimizing communication efficiency and ensuring the security of model updates against adversarial attacks. Critical future developments include expanding its use cases across various industries and integrating it with blockchain technologies for enhanced data provenance and incentive mechanisms.

An advanced Distributed Ledger Technology DLT infrastructure prototype showcases a sleek, off-white textured exterior enveloping intricate blue metallic internal mechanisms. Vibrant electric blue luminescence emanates from gears and structural elements, symbolizing active Zero-Knowledge Proof ZKP computation and efficient hash rate optimization. This validator node architecture suggests a robust design for decentralized network operations, potentially facilitating cross-chain interoperability and secure smart contract execution within a scalable Layer-2 scaling solution framework.

→Learning Consensus

→Block Structure Design

→Blockchain Security Model

Zero-Knowledge Proof of Training Secures Decentralized Federated Learning Consensus

ZKPoT uses zk-SNARKs to verify decentralized model accuracy without revealing private data, solving the efficiency-privacy trade-off in federated learning.