Blockchain Model Storage for Federated Learning Traceability
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Solution Overview
Problem
Current blockchain-based federated learning systems face challenges in efficiently organizing and accessing model data, storing different versions of models, and facilitating model sharing and deployment, which hinders the traceability, accountability, and scalability of model training and deployment processes.
Innovation Solution
The implementation of a blockchain storage service (BSS), blockchain access service (BAS), model repository (MR), and model deployment and scoring service (MDSS) facilitates efficient data storage, access, and deployment of models within a blockchain system, allowing for flexible storage of full and tailored model versions, automatic tailoring, and decentralized model scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blockchain technology is used to store model data in federated learning systems, then traceability and accountability are improved, but storage efficiency and access speed deteriorate due to the inherent complexity of blockchain operations
Solution Approach 1:
The patent segments model data into different versions and stores them in a structured manner within the blockchain system. The model repository organizes models by version numbers, allowing efficient retrieval of specific versions while maintaining the complete audit trail in the blockchain. This segmentation resolves the contradiction by enabling targeted access to specific model versions without requiring traversal of the entire blockchain history.
Solution Approach 2:
The patent introduces a model repository as an intermediary layer between the blockchain storage and the federated learning participants. This repository provides optimized access paths for retrieving model versions while the blockchain maintains the authoritative record for traceability. The intermediary resolves the contradiction by caching frequently accessed model data locally, reducing blockchain interaction overhead while preserving audit capabilities.
2Adaptability or versatility
If all versions of models are stored in the blockchain system, then model sharing and deployment flexibility are improved, but storage requirements and system complexity increase
Solution Approach 1:
The patent creates a universal model repository that serves multiple functions: storing all model versions, providing access control, enabling model sharing, and supporting deployment operations. This single multi-functional system resolves the contradiction by consolidating what would otherwise require multiple separate systems, reducing overall complexity while maintaining versatility in model sharing and deployment.
Solution Approach 2:
The patent adds a dimensional organization to model storage by introducing version numbers and categorization schemes. Instead of storing models in a flat blockchain structure, models are organized in a multi-dimensional space with dimensions for version, type, and access permissions. This dimensional organization enables efficient retrieval and sharing while keeping the system manageable through structured metadata.
3Ease of operation
If clients perform model scoring operations, then model deployment capability is improved, but computational burden on clients increases
Solution Approach 1:
The patent extracts the computationally intensive model scoring operations from client devices and relocates them to the blockchain network or centralized model deployment service. Clients only need to submit scoring requests and receive results, while the actual computation is performed by nodes with sufficient computational resources. This extraction resolves the contradiction by maintaining deployment capability while eliminating the computational burden from resource-constrained client devices.
Data Source
AI summary
Procedures, methods, architectures, apparatuses, systems, devices, and computer program products directed to blockchain-enabled model storage, sharing and deployment for supporting federated learning are provided. Among the methods is a method directed to blockchain-enabled storage of distributed learning data that may include receiving information indicating a blockchain storage request, including information associated with a distributed learning task; obtaining information identifying one or more blockchains based on a blockchain storage solution, wherein the blockchain storage solution is based on the information indicating a blockchain storage request; determining blockchain-related instructions based on the blockchain storage solution, wherein the blockchain-related instructions comprise at least some of the information identifying one or more blockchains; and transmitting the blockchain-related instructions to a plurality of distributed participant nodes.