Federated Learning Model Updates via Decentralized Aggregation
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Solution Overview
Problem
Federated learning systems face privacy leakage and data reconstruction attacks due to the misconception that model updates contain less information than raw training data, with existing techniques like differential privacy and cryptographic primitives being computationally expensive or decreasing model accuracy.
Innovation Solution
A multi-layered defense strategy involving trustworthy aggregation through trusted execution environments, decentralized aggregation with model partitioning, and dynamic permutation to protect against information leakage, ensuring that even if aggregators are breached, adversaries cannot reconstruct the training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If differential privacy aggregation through addition of statistical noise is used, then privacy protection is improved, but model accuracy significantly decreases
Solution Approach 1:
The patent segments model updates into multiple partitions and distributes them across multiple aggregators. Each aggregator processes only a subset of partitions, preventing any single entity from reconstructing complete model updates. This segmentation approach provides privacy protection without requiring noise addition, thus maintaining model accuracy.
Solution Approach 2:
The patent introduces trusted execution environments (TEEs) as intermediary components between clients and aggregators. TEEs provide secure computation and data isolation, enabling privacy-preserving aggregation without statistical noise. The TEEs act as mediators that protect data confidentiality while allowing accurate model training.
2Object-affected harmful factors
If cryptographic primitives such as SMC protocols or Homomorphic Encryption are used, then privacy protection is improved, but computational cost significantly increases
Solution Approach 1:
The patent replaces heavy cryptographic primitives with a lighter-weight approach using trusted execution environments. Instead of employing computationally intensive SMC protocols or homomorphic encryption, the system uses TEEs to provide secure computation, significantly reducing computational overhead while maintaining privacy protection.
Solution Approach 2:
By segmenting model updates across multiple aggregators, each operating within TEEs, the system distributes computational workload and reduces the cryptographic operations required at any single location. This segmentation enables more efficient processing compared to centralized cryptographic approaches.
3Device complexity
If a single central aggregation server is used, then system simplicity is improved, but reliability decreases due to single point of failure
Solution Approach 1:
The patent segments the single aggregation server into multiple distributed aggregators, each operating independently within trusted execution environments. This distribution eliminates the single point of failure while maintaining system functionality. Each aggregator processes a subset of model updates, ensuring continued operation even if some aggregators fail or are compromised.
Solution Approach 2:
The patent transitions from a centralized single-point aggregation model to a distributed multi-point aggregation model, adding a dimensional aspect of distribution. This dimensional change from one central server to multiple aggregators improves reliability while maintaining manageable system complexity through structured deployment.
4Productivity
If model updates are transmitted directly without partitioning, then communication efficiency is improved, but privacy protection decreases
Solution Approach 1:
The patent segments model updates into multiple partitions that are distributed to different aggregators. This segmentation prevents any single aggregator from accessing complete model updates, thereby protecting privacy. The partitioning is implemented efficiently with minimal overhead, maintaining communication effectiveness while enhancing security.
Data Source
AI summary
Techniques for distributed federated learning leverage a multi-layered defense strategy to provide for reduced information leakage. In lieu of aggregating model updates centrally, an aggregation function is decentralized into multiple independent and functionally-equivalent execution entities, each running within its own trusted executed environment (TEE). The TEEs enable confidential and remote-attestable federated aggregation. Preferably, each aggregator entity runs within an encrypted virtual machine that support runtime in-memory encryption. Each party remotely authenticates the TEE before participating in the training. By using multiple decentralized aggregators, parties are enabled to partition their respective model updates at model-parameter granularity, and can map single weights to a specific aggregator entity. Parties also can dynamically shuffle fragmentary model updates at each training iteration to further obfuscate the information dispatched to each aggregator execution entity. This architectural prevents the aggregator from being a single point-of-failure, and serves to protect the model even if all aggregators are compromised.