Federated Learning Aggregation Trust Verification
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Solution Overview
Problem
Existing federated learning systems face challenges in verifying the trustworthiness of aggregation schemes, as they cannot reliably ensure that each parameter of the global model originated from an individual real client, leading to potential manipulation by aggregators.
Innovation Solution
A method involving clients generating bit masks indicating which parameters of the global model correspond to their local models, using homomorphic additive encryption to combine these masks into a matrix, and analyzing the resulting mask to determine the aggregator's honesty by checking for values of only '1', ensuring each parameter can be traced back to a real client.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning uses decentralized training across multiple clients, then data privacy is protected and collaboration is enabled, but the trustworthiness of the aggregation scheme cannot be verified
Solution Approach 1:
The verification mechanism is segmented into independent bit masks for each client, where each mask tracks the origin of specific parameters. This segmentation allows individual verification of parameter sources without requiring complex centralized oversight, thereby verifying aggregation trustworthiness while maintaining decentralized simplicity.
Solution Approach 2:
Bit masks serve as intermediary data structures that mediate between clients and the aggregation scheme. These masks carry verification information without revealing sensitive data, enabling trust verification while preserving the decentralized federated learning architecture and avoiding complex direct verification mechanisms.
2Reliability
If the aggregator combines parameters from multiple clients to create a global model, then collaborative learning is achieved, but manipulation of parameter origins cannot be detected
Solution Approach 1:
The verification mechanism extracts only the essential origin information needed for verification - represented as bit masks indicating which parameters come from which clients. This extraction provides parameter origin verification while leaving the actual private data behind, preventing information loss of sensitive local data.
Solution Approach 2:
Each client maintains local bit masks that track the origin quality of their contributed parameters. This local quality tracking enables verification of parameter origins without requiring centralized access to private data, thereby verifying reliability while preserving data privacy through localized verification.
3Reliability
If bit masks are combined using homomorphic additive encryption, then privacy is maintained during verification, but computational complexity increases
Solution Approach 1:
The verification process changes the parameter representation to encrypted bit masks that can be combined using homomorphic additive encryption. This parameter transformation enables privacy-pres verification through cryptographic operations, where the energy cost is offset by the elimination of more complex secure multi-party computation protocols.
Data Source
AI summary
A computer-implemented method, system and computer program product for verifying the trustworthiness of an aggregation scheme utilized by an aggregator in the federated learning technique. A bit mask is received from each client used for training a machine learning algorithm using the federated learning technique. Such a bit mask contains values of ones and zeros, where a value of one indicates that the updated parameter of the global model corresponds to a parameter used by the local model trained on the client and a value of zero indicates that is not the case. These bit masks, which are encrypted, may then be combined using a homomorphic additive encryption scheme into a mask containing a matrix of values. If the mask contains a matrix of values of only the value of one, then the aggregator is deemed to be trustworthy. Otherwise, the aggregator is deemed to be untrustworthy.