SYSTEM AND METHOD FOR REAL-TIME MACHINE LEARNING IN DISTRIBUTED NETWORKS

Abstract

The present invention provides a system and method for enabling real-time machine learning within distributed networks through decentralized data processing and local model training. The system comprises a plurality of edge devices that preprocess and analyze data locally, generating machine learning models based on the processed information. These edge devices periodically transmit model updates to a central server, which aggregates the updates to create an optimized global model, ensuring minimal latency and efficient resource utilization. Employing a federated learning framework, the invention preserves data privacy by minimizing the transmission of raw data. The system incorporates adaptive model aggregation techniques and anomaly detection capabilities to enhance responsiveness and fault tolerance. This innovative architecture is well-suited for applications requiring timely insights and decision-making, such as smart cities, industrial monitoring, and autonomous systems, providing a scalable, secure, an

Specification

Description:The embodiments of the present invention generally relates to the field of machine learning and distributed computing, specifically to a system and method that enables real-time machine learning across distributed networks. This system leverages decentralized data processing and model training in edge and cloud-based architectures to facilitate rapid model updates and low-latency responses in applications such as Internet of Things (IoT), autonomous systems, and other data-intensive environments that require timely insights.

BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

With the rapid expansion of co , Claims:1. A system for real-time machine learning in a distributed network, comprising:
a plurality of edge devices, each configured to locally preprocess data and generate a local machine learning model based on the preprocessed data;
a central server configured to receive and aggregate local model updates from each edge device to form a global model, the central server periodically transmitting the global model back to each edge device;
a communication interface between the edge devices and the central server to facilitate the transmission of local model updates and global model updates;
wherein the system is configured to provide real-time machine learning capabilities by continuously updating the global model based on current data from each edge device, thereby minimizing latency and enabling privacy-preserving processing by limiting the transmission of raw data.

2. The system of Claim 1, wherein each edge device is configured to apply data compression and filtering techniques to the preprocessed data to reduce

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