Importance-Based Parameter Transmission for Federated Learning Convergence
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Solution Overview
Problem
Existing federated learning systems face resource consumption and inefficiencies in transmitting model updates between a parameter server and agent entities, leading to increased delays and energy consumption, with existing methods like sparsification and quantization compromising model convergence performance.
Innovation Solution
Adapt transmission and reception of model parameter vectors based on importance scores, determining the significance of each component using techniques like measuring output variation and channel conditions, to optimize resource use and improve convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sparsification or quantization is applied to model updates, then transmission resources are reduced, but model convergence performance deteriorates
Solution Approach 1:
The patent applies different transmission strategies to different components of the model parameter vector based on their importance scores. High-importance components receive more precise transmission (higher quantization precision or no sparsification), while low-importance components use reduced precision or sparsification. This local differentiation resolves the contradiction by optimizing the balance between transmission efficiency and convergence performance for each parameter component.
2Reliability
If full model parameter vectors are broadcast, then all agents receive complete information, but network energy consumption and transmission delays increase
Solution Approach 1:
The patent segments the model parameter vector into multiple components and transmits them selectively based on importance scores. Instead of broadcasting the complete parameter vector to all agents, the system identifies and transmits only the most important components (e.g., top-k components with highest importance scores). This segmentation reduces transmission energy consumption while maintaining sufficient information for model convergence.
3Productivity
If importance scores are computed for each parameter component, then transmission can be optimized, but computational complexity increases
Solution Approach 1:
The patent employs self-service mechanisms where agents compute importance scores for parameter components using their local data and models. The importance scoring can be based on intrinsic properties of the parameters (e.g., magnitude, sparsity patterns) or extrinsic factors (e.g., channel conditions, agent priorities). This self-service approach distributes the computational burden and enables optimization without requiring centralized complex analysis.
Data Source
AI summary
There is provided techniques for an iterative learning process being performed between a server entity and agent entities. The iterative learning process pertains to a computational task to be performed by the agent entities. The computational task pertains to the agent entities participating in training a machine learning model. For each iteration round of the iterative learning process the server entity sends a global parameter vector of the computational task to the agent entities. For each iteration round of the iterative learning process a local model parameter vector with locally computed computational results is sent per agent entity to the server entity. The locally computed computational results are updates of the machine learning model.