Federated Learning Gradient Timing for Privacy and Load Balancing
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Solution Overview
Problem
Existing federated learning techniques face challenges in maintaining data privacy while training global models with decentralized data, requiring significant server-side overhead and making unrealistic assumptions about client participation and availability.
Innovation Solution
Implementing a decentralized training approach where client devices randomly determine reporting times within a window to transmit gradients, ensuring privacy amplification with minimal server-side orchestration, allowing even distribution of gradients and reducing resource overload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If client devices transmit gradients at synchronized times, then the global model can be updated efficiently, but network spikes and resource overload occur
Solution Approach 1:
The system implements periodic gradient transmission where client devices transmit gradients at randomly determined intervals within a reporting window, rather than continuously or synchronously. This periodic action with random timing distributes network traffic over time, preventing network spikes while maintaining model update efficiency.
Solution Approach 2:
The reporting window and transmission timing are made dynamic rather than fixed. The server configures reporting windows, and client devices independently determine random reporting times within these windows. This dynamic timing strategy adapts to varying device availability and network conditions, preventing resource overload.
2Productivity
If the server coordinates all client reporting times, then gradient distribution can be optimized, but server-side overhead increases significantly
Solution Approach 1:
Client devices autonomously determine their own reporting times within server-configured windows using local randomness. Each device independently selects its reporting time without server coordination, eliminating the need for complex server-side scheduling while still achieving balanced gradient distribution across the network.
Solution Approach 2:
The server acts as a mediator by configuring reporting windows and selecting participating clients, but delegates the actual timing decision to client devices. This intermediary role reduces server overhead from active coordination to passive configuration, optimizing gradient distribution without significant server-side complexity.
3Productivity
If all selected clients participate in each training round, then model convergence is faster, but device availability assumptions become unrealistic
Solution Approach 1:
The system accepts partial participation from client devices in each training round rather than requiring full participation. By using random sampling and reporting windows, the system achieves sufficient model convergence with a subset of available devices, making the approach adaptable to real-world device availability without unrealistic assumptions.
Data Source
AI summary
Techniques are disclosed that enable training a global model using gradients provided to a remote system by a set of client devices during a reporting window, where each client device randomly determines a reporting time in the reporting window to provide the gradient to the remote system. Various implementations include each client device determining a corresponding gradient by processing data using a local model stored locally at the client device, where the local model corresponds to the global model.