Federated Learning Parameter Permutation with PIR Privacy Protection
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Solution Overview
Problem
Federated learning systems face challenges in providing data privacy for clients' training data and ensuring robustness against malicious clients, with existing techniques either compromising privacy or model utility.
Innovation Solution
Intra-model parameter shuffling combined with Private Information Retrieval (PIR) techniques to encrypt and shuffle model updates, allowing secure aggregation while maintaining privacy and robustness against poisoning attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional federated learning is used to train machine learning models, then model training can be performed distributedly, but client data privacy is compromised and the system is vulnerable to poisoning attacks
Solution Approach 1:
The patent segments the model parameters into individual elements that can be independently shuffled and processed. Each client's parameter updates are broken down into discrete components that undergo separate permutation operations, enabling fine-grained privacy protection while maintaining the overall federated learning structure
Solution Approach 2:
The patent applies preliminary permutation and masking operations to model parameters before they are transmitted or aggregated. By pre-shuffling and masking parameters at the client side before transmission, the system prevents privacy leaks and poisoning attacks from occurring in the first place, rather than relying on post-processing detection
2Reliability
If encryption techniques are applied to protect parameter privacy, then data security is improved, but computational overhead increases
Solution Approach 1:
The patent employs computationally inexpensive permutation and masking operations that can be applied and discarded easily. These operations use simple mathematical transformations rather than heavy cryptographic encryption, providing adequate security protection with minimal computational cost
Solution Approach 2:
The patent transforms model parameters through permutation and masking operations that change their representation without requiring full encryption. By modifying parameter positions and applying masking values, the system achieves security through parameter transformation rather than computationally intensive encryption
3Reliability
If parameter randomization is applied to prevent poisoning attacks, then model robustness is improved, but model utility may be degraded
Solution Approach 1:
The patent inverts the approach by applying permutation and masking to protect parameters, then systematically reversing these operations during aggregation and model updates. This inversion ensures that the protective randomization does not permanently degrade model utility, as the original parameter relationships are restored through the reverse permutation process
Solution Approach 2:
The patent incorporates feedback mechanisms where the permutation patterns and masking operations are tracked and reversed in subsequent processing steps. By maintaining feedback loops that record and undo the randomization transformations, the system preserves model utility while maintaining robustness against poisoning attacks
Data Source
AI summary
Parameter permutation is performed for federated learning to train a machine learning model. Parameter permutation is performed by client systems of a federated machine learning system on updated parameters of a machine learning model that have been updated as part of training using local training data. An intra-model shuffling technique is performed at the client systems according to a shuffling pattern. Then, the encoded parameters are provided to an aggregation server using Private Information Retrieval (PIR) queries generated according to the shuffling pattern.