Federated Learning Deployment Scheme for Parallel Work Nodes
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Solution Overview
Problem
Federated learning algorithms face inefficiencies due to low utilization of computing resources, as they typically run on a single node and do not support parallel computing, limiting their ability to handle large datasets effectively.
Innovation Solution
A method is introduced to generate a deployment scheme that runs multiple work nodes on the same computing device, allowing for efficient allocation of resources and parallel processing, thereby improving the efficiency of federated learning by fully utilizing computing resources for joint modeling of large data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning algorithms run on a single node to ensure data security through cryptography, then data privacy is protected, but algorithm efficiency is greatly reduced
Solution Approach 1:
The patent segments the federated learning system into multiple work nodes (first work node, second work node, etc.) that can run in parallel on the same computing device. Each work node processes different portions of the training task, enabling parallel computation while maintaining cryptographic security through the federated learning protocol. This resolves the contradiction by allowing efficiency improvement through parallelization without compromising the single-node security model.
2Device complexity
If federated learning algorithms run on a single computing node, then implementation is simple, but computing resource utilization is low
Solution Approach 1:
The patent divides the computing device into multiple virtual work nodes that can be managed independently. Each work node is assigned specific computing tasks and resources, allowing the system to utilize multiple cores or processing units within the same device. This segmentation enables high resource utilization while keeping the overall implementation approach relatively simple through automated deployment schemes.
Solution Approach 2:
The patent transitions from a single-dimensional (single node) execution model to a multi-dimensional model by creating multiple work nodes within the same computing device. This dimensional expansion allows parallel processing across multiple virtual nodes while physically remaining on one device, thereby improving resource utilization without proportionally increasing physical complexity.
3Device complexity
If federated learning tasks are executed without parallel computing support, then system design is straightforward, but the ability to handle large datasets is limited
Solution Approach 1:
The patent segments large datasets into multiple partitions that can be processed in parallel by different work nodes. Each work node handles a specific subset of the data, enabling the system to process large datasets efficiently. The segmented approach maintains straightforward system design through standardized node interfaces while dramatically increasing dataset processing capacity.
Data Source
AI summary
A federated learning method and apparatus, a device and a medium are provided, and relates to the field of artificial intelligence, in particular to the field of federated learning and machine learning. The federated learning method includes: receiving data related to a federated learning task of a target participant, wherein the target participant at least includes a first computing device for executing the federated learning task; determining computing resources of the first computing device that are able to be used to execute the federated learning task; and generating a first deployment scheme for executing the federated learning task in response to determining that the data and the computing resources meet a predetermined condition, wherein the first deployment scheme instructs to generate at least a first work node and a second work node on the first computing device.