Definition ∞ Gradient aggregation is a process in machine learning where local model updates are combined into a global model. This technique is commonly employed in federated learning, where multiple decentralized clients train machine learning models on their local datasets without sharing raw data. Instead, clients compute gradients from their local data and transmit these gradients to a central server. The server then combines these received gradients to update the main global model, distributing the refined model back to the clients. This method maintains data privacy while improving overall model performance.
Context ∞ Gradient aggregation is a key mechanism for enabling privacy-preserving machine learning applications, particularly relevant in sectors dealing with sensitive user data or decentralized data sources. Its application extends to optimizing AI models within blockchain networks or decentralized applications where data residency and privacy are paramount. Current research focuses on enhancing the efficiency and security of aggregation methods to prevent data leakage and adversarial attacks during the gradient transmission and combination phases. The method’s utility for collaborative AI development without centralizing data holds considerable promise.
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