Federated Learning Malicious Participant Identification via Dataset Sketch
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Solution Overview
Problem
Federated learning systems face challenges in identifying and filtering out malicious participants, as existing solutions rely on assumptions of identical independent distribution (iid) of datasets, which do not hold in heterogeneous federated learning environments, leading to ineffective differentiation between malicious and benign updates.
Innovation Solution
The implementation of a dataset sketch commitment mechanism, where each participant generates a vector data structure with statistical characteristics of their local dataset, allowing for grouping of participants with similar datasets and robust aggregation techniques, thereby identifying and filtering out potentially malicious participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing malicious participant identification solutions are used in federated learning, then they assume identical independent distribution (iid) of datasets, but this assumption does not hold in heterogeneous federated learning environments, leading to ineffective differentiation between malicious and benign updates
Solution Approach 1:
The patent changes the parameters used for identifying malicious participants from assuming iid distribution to using dataset sketch commitment data structures that capture statistical characteristics. This allows the system to adapt to heterogeneous datasets by comparing actual dataset properties rather than relying on unrealistic distributional assumptions.
Solution Approach 2:
The patent introduces dataset sketch commitment data structures as an intermediary mechanism. These structures serve as a mediator that enables comparison and verification of dataset characteristics without requiring direct access to raw data, thus facilitating malicious participant identification in heterogeneous environments while preserving data privacy.
2Productivity
If all participant updates are aggregated in federated learning, then the federated model incorporates contributions from all participants, but malicious updates disrupt the training process and reduce model quality
Solution Approach 1:
The patent applies preliminary action by verifying dataset sketch commitment data structures before aggregating model updates. This pre-verification step identifies and filters out malicious participants before their updates can contaminate the federated model, ensuring that only legitimate contributions are incorporated while maintaining training efficiency.
3Reliability
If dataset sketch commitment data structures are used to identify malicious participants, then benign updates are filtered effectively, but computation efforts are increased due to analysis of commitment data structures
Solution Approach 1:
The patent extracts only the essential statistical characteristics of datasets into compact commitment data structures. This extraction approach enables effective verification of dataset authenticity without requiring analysis of the entire dataset, thus maintaining high verification accuracy while significantly reducing computational energy requirements compared to full dataset analysis.
Data Source
AI summary
Mechanisms for performing intelligent federated machine learning (ML) model updates are provided. A plurality of ML model updates, and a plurality of dataset sketch commitment data structures (sketches), are received from a plurality of participant computing systems. Each sketch provides statistical characteristics of a corresponding local dataset used by a corresponding participant to train a local ML model. A potentially malicious participant identification operation is performed based on an analysis of the plurality of sketches to identify one or more potentially malicious participants based on differences in sketches. ML model updates received from participant computing systems identified as potentially malicious participants are discarded to thereby generate a modified set of updates. The federated ML computer model is updated based on the modified set of updates.