Federated Learning Parameter Permutation for Private Model Aggregation
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Solution Overview
Problem
Federated learning systems face challenges in providing data privacy for clients' training data while ensuring robustness against malicious clients, with existing methods 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 and protection against poisoning attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If encrypted parameters are used for federated learning, then data privacy is improved, but model utility deteriorates
Solution Approach 1:
The model parameters are segmented into individual parameter units that can be independently shuffled and encrypted. This allows selective processing of parameter subsets, enabling privacy protection through encryption while maintaining model utility by preserving the structural relationships within parameter groups through controlled shuffling operations.
Solution Approach 2:
Parameter shuffling is performed as a preliminary action before encryption and aggregation. By randomizing the order of parameters beforehand, the system establishes privacy protection in advance while maintaining the ability to reconstruct meaningful model updates during aggregation, thus preventing information leakage without compromising model utility.
2Reliability
If parameter shuffling is applied, then privacy amplification is improved, but model training accuracy deteriorates
Solution Approach 1:
Different shuffling patterns are applied to different parameter groups or layers of the model, allowing localized optimization of privacy protection while preserving local model accuracy. This selective shuffling approach ensures that critical parameters maintain their relationships for accurate training while less sensitive parameters receive stronger privacy protection through more aggressive shuffling.
3Reliability
If PIR queries are used for secure aggregation, then security against poisoning attacks is improved, but system complexity deteriorates
Solution Approach 1:
The PIR protocol acts as an intermediary mechanism between clients and the aggregation server, enabling secure parameter retrieval without direct exposure of sensitive model updates. This intermediary layer provides cryptographic protection against poisoning attacks while abstracting the complexity of secure aggregation from the federated learning workflow, making the system more manageable despite the underlying cryptographic complexity.
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.