Reward-Driven Federated Learning via Smart Contract Verification
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Solution Overview
Problem
Federated learning faces challenges in maintaining privacy and incentivizing model contributors, with current solutions lacking transparent and fair reward schemes, and being vulnerable to malicious nodes and low contribution from individual nodes.
Innovation Solution
A reward-driven approach using a unique smart contract design on a distributed ledger to calculate and reward honest participants' contributions, while penalizing malicious ones, by computing a scalar quantity representing the deviation of local and global machine learning models and issuing rewards or fees based on these contributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If distributed ledgers are used to preserve privacy and enable consensus, then privacy of transactions is improved, but the difficulty of solving the consensus problem while maintaining privacy increases
Solution Approach 1:
The system segments the consensus process by introducing validator nodes that specialize in verifying cryptographic proofs and model updates, separating the consensus function from the privacy-preserving computation. This allows the ledger to maintain privacy while achieving consensus through specialized validator roles that check contributions without exposing sensitive data.
Solution Approach 2:
The patent introduces an intermediary reward distribution mechanism that mediates between privacy-preserving federated learning operations and the distributed ledger's consensus requirements. The smart contract system acts as an intermediary layer that verifies contributions cryptographically and distributes rewards without requiring nodes to expose their private data or model parameters.
2Loss of information
If consortium-trusted aggregation server is used to maintain privacy, then privacy of individual models is improved, but the risk of lack of contribution from individual nodes increases
Solution Approach 1:
The system implements feedback mechanisms through the distributed ledger that track and verify each node's contributions to the federated learning process. Smart contracts automatically record model updates, validate their authenticity, and provide transparent feedback on contribution levels, incentivizing nodes to actively participate while preserving model privacy through cryptographic verification.
Solution Approach 2:
The patent changes the verification parameter from trusting the aggregation server to verifying cryptographic proofs of contribution on the distributed ledger. Each node's contribution is transformed into a verifiable cryptographic parameter that can be checked by the network without revealing the underlying model data, thus maintaining privacy while ensuring contribution reliability.
3Reliability
If aggregation server detects and drops malicious contributors, then model efficacy is improved, but the complexity of detection and management increases
Solution Approach 1:
The system performs preliminary verification of node contributions through cryptographic proofs before they are incorporated into the global model. The distributed ledger and smart contracts pre-validate model updates, checking for malicious behavior patterns before aggregation, which simplifies the overall management complexity by catching issues early in the process rather than requiring complex post-detection mechanisms.
Solution Approach 2:
The patent replaces the mechanical system of server-based detection and manual management with cryptographic verification mechanisms embedded in the distributed ledger. Instead of the aggregation server actively detecting and managing contributors, the system uses cryptographic proofs and smart contracts to automatically verify contributions and enforce rules, reducing management complexity through decentralized automated verification.
4Ease of operation
If transparent and incentive-driven contribution is implemented, then fairness of reward distribution is improved, but the complexity of incentive scheme design increases
Solution Approach 1:
The smart contract system serves multiple functions simultaneously: it verifies contributions, calculates rewards, distributes incentives, and maintains the distributed ledger all through a single universal mechanism. This multi-functionality reduces the overall complexity compared to having separate systems for each function, as the smart contracts provide a unified framework that handles transparency and incentive distribution together.
Solution Approach 2:
The system implements self-service through automated smart contracts that independently calculate and distribute rewards based on verified contributions. The incentive scheme operates autonomously without requiring manual intervention or complex centralized management, as the smart contracts automatically enforce the reward distribution rules based on objectively verified contribution data from the distributed ledger.
Data Source
AI summary
Systems and methods for federated learning based on a reward-driven approach are disclosed. In one embodiment, a method may include: (1) receiving, by a federated contribution computer program executed by a federated node in a distributed ledger network, a plurality of local machine learning model updates from a plurality of clients in the distributed ledger networks; (2) retrieving, by the federated contribution computer program, a prior global machine learning model; (3) calculating, by the federated contribution computer program, a current global machine learning model based on the prior global machine learning model and the plurality of local machine learning model updates; (4) determining, by the federated contribution computer program, a federated contribution for each client based on each client's federated contribution to the current global machine learning model; and (5) issuing, by the federated contribution computer program, rewards to each client based on the client's federated contribution.