Federated Global Batch Normalization via Secure Aggregation
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Solution Overview
Problem
Federated learning systems face challenges in sharing batch normalization statistics across nodes while maintaining privacy, as local parameters are sensitive and not conventionally shared, limiting the ability to generate and utilize global batch normalization statistics.
Innovation Solution
A federated global batch normalization layer and associated communication/aggregation protocol that allows nodes to maintain running sums and squares of inputs, enabling secure aggregation of gradients and statistics at a central node to compute global mean and variance without sharing actual data, thus updating batch normalization layers globally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If local batch normalization parameters are shared across nodes, then global batch normalization statistics can be generated, but privacy and security of individual nodes are compromised
Solution Approach 1:
The patent introduces secure aggregation as an intermediary mechanism that allows nodes to contribute to global batch normalization statistics without directly sharing their local parameters. The central node aggregates gradients and computes global statistics through this secure channel, preventing direct exposure of private data while enabling collaborative learning.
Solution Approach 2:
Instead of sharing actual local parameters, the system shares aggregated gradient information and computed statistics. The global batch normalization layer is updated using these aggregated copies of information rather than original data, allowing model improvement without compromising individual node privacy.
2Object-affected harmful factors
If nodes maintain local batch normalization parameters, then privacy is protected, but the ability to benefit from collective learning is limited
Solution Approach 1:
The system implements a feedback mechanism where the central node receives gradient information from nodes, computes global batch normalization statistics, and sends updated parameters back to nodes. This feedback loop enables nodes to benefit from collective learning while maintaining their local data privacy through the secure aggregation protocol.
Solution Approach 2:
The patent merges local batch normalization computations with global aggregation by combining gradients from multiple nodes at the central node. This merging process enables the system to leverage data from all nodes for global statistics while keeping individual node data separate and private throughout the computation process.
3Measurement precision
If actual data is shared to compute global statistics, then accurate global batch normalization can be achieved, but data security and privacy are compromised
Solution Approach 1:
The patent replaces the mechanical approach of directly sharing and processing actual data with a computational approach using gradient-based aggregation. Instead of moving raw data to compute statistics, the system substitutes this with aggregating gradient information and computing statistics through mathematical operations on these gradients, achieving the same result with enhanced security.
Data Source
AI summary
Federating batch normalization layers in federated learning is disclosed. Statistics including a sum of inputs to a batch normalization layer, a sum of squares of inputs to the batch normalization layer, and a sum of a number of inputs to a local model are tracked and aggregated with similar sums from other nodes. A global mean and a global variance are generated from the aggregated sums and synchronized back to local models such that the bath normalization layers of the local models are federated.