Adaptive Federated Learning Compression for Private Training
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Solution Overview
Problem
Federated learning systems face challenges in achieving communication efficiency while maintaining privacy, as existing compression techniques require fixed compression rates and do not adapt to changing error conditions, leading to suboptimal communication and accuracy tradeoffs.
Innovation Solution
The implementation of adaptive compression methods that automatically adjust compression rates based on error values during training, using techniques like count-mean sketching and secure aggregation to ensure provable privacy guarantees and optimal communication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compression techniques are applied to reduce communication size, then communication efficiency is improved, but manufacturing precision (compression accuracy) deteriorates
Solution Approach 1:
The patent implements adaptive compression where the compression rate dynamically adjusts based on the current compression error. The system monitors compression errors during training and automatically modifies the compression rate to maintain optimal accuracy while maximizing communication efficiency at different training stages.
Solution Approach 2:
The system employs feedback mechanisms by calculating compression errors and using this information to adjust compression parameters. The compression error feedback loop allows the system to learn from previous compression outcomes and optimize future compression operations, balancing accuracy and communication efficiency.
2Device complexity
If fixed compression rate is used, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The compression system performs self-adjustment by automatically monitoring its own compression errors and modifying its compression rate without external intervention. This self-service capability allows the system to adapt to changing training conditions while maintaining reasonable operational simplicity.
Solution Approach 2:
The system transitions from static fixed compression rates to dynamic adaptive compression rates that automatically respond to training progress and error conditions, enabling the compression behavior to evolve throughout the training process.
3Reliability
If larger ML models are used, then model capability is improved, but communication efficiency deteriorates
Solution Approach 1:
The patent changes the compression parameter (compression rate) based on the model size and training progress. For larger models, the system adjusts compression parameters dynamically to maintain effective communication despite increased message sizes, allowing larger models to be trained efficiently.
Data Source
AI summary
New techniques are provided which reduce communication in private federated learning without the need for setting or tuning compression rates. Example on-the-fly methods automatically adjust the compression rate based on the error induced during training, while maintaining provable privacy guarantees through the use of secure aggregation and differential privacy.