Federated Learning Aggregation via Encryption Vector Key Retrieval
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Solution Overview
Problem
Federated learning systems face privacy risks due to inference attacks and require significant communication overhead for privacy guarantees, often relying on a trusted aggregator that exposes participants to unnecessary risk.
Innovation Solution
A method involving the distribution of public encryption keys to participants, allowing them to encrypt responses based on local training data, with an aggregator generating an aggregation vector and retrieving a private key to create an aggregated model efficiently, while maintaining high security and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning systems use traditional centralized training approaches, then model training can be performed efficiently, but privacy risks increase due to data sharing requirements
Solution Approach 1:
The system segments the training process into local model training at participant devices and centralized aggregation at the server. Each participant trains a local model using their own data, then shares only model parameters (not data) with the aggregator. This segmentation maintains privacy while enabling collaborative learning across multiple devices.
Solution Approach 2:
The aggregator acts as an intermediary that receives encrypted model parameters from participants, aggregates them using secure multi-party computation protocols, and generates updated model parameters without ever accessing the raw training data. This intermediary mechanism enables privacy-preserving collaboration while maintaining system efficiency.
2Reliability
If federated learning systems implement privacy-preserving techniques such as differential privacy, then privacy guarantees are improved, but communication overhead increases significantly
Solution Approach 1:
The system changes the parameters being communicated from raw training data to compressed model parameters (weights and biases). By transmitting only the essential model updates rather than entire datasets, communication overhead is dramatically reduced while privacy is preserved through the aggregation protocol and differential privacy mechanisms.
Solution Approach 2:
The system implements partial differential privacy by applying privacy protections selectively to the most sensitive aggregation operations rather than uniformly to all communications. This selective approach maintains necessary privacy guarantees while minimizing the communication overhead associated with full differential privacy implementation.
3Productivity
If federated learning systems rely on a trusted aggregator, then model aggregation can be performed centrally, but security risks increase for individual participants
Solution Approach 1:
The system applies preliminary protective measures by encrypting model parameters before transmission to the aggregator and using secure multi-party computation protocols during aggregation. These pre-established security mechanisms prevent potential attacks on the aggregator and protect participant data without reducing aggregation efficiency.
Solution Approach 2:
The system implements feedback mechanisms where participants can verify the integrity of aggregation operations and report potential security issues. This continuous monitoring and verification process enhances security while maintaining the efficiency of centralized aggregation by enabling early detection and response to potential threats.
4Reliability
If federated learning systems require rekeying when participants join or leave, then security can be maintained, but scalability and flexibility decrease
Solution Approach 1:
The system uses universal cryptographic protocols and aggregation mechanisms that work consistently regardless of participant changes. The secure multi-party computation framework and differential privacy mechanisms remain effective whether participants join or leave, eliminating the need for rekeying while maintaining security and enabling flexible system evolution.
Data Source
AI summary
A plurality of public encryption keys are distributed to a plurality of participants in a federated learning system, and a first plurality of responses is received from the plurality of participants, where each respective response of the first plurality of responses was generated based on training data local to a respective participant of the plurality of participants and is encrypted using a respective public encryption key of the plurality of public encryption keys. A first aggregation vector is generated based on the first plurality of responses, and a first private encryption key is retrieved using the first aggregation vector. An aggregated model is then generated based on the first private encryption key and the first plurality of responses.