RL-Based Dynamic Aggregation for Distributed Learning
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Solution Overview
Problem
Existing distributed learning (DL) and federated learning (FL) methodologies use statically defined aggregation functions, which are not always optimal for training speed, resource usage, or other training goals.
Innovation Solution
A reinforcement learning (RL)-based framework that dynamically changes the aggregation function used by the parameter server over the course of a DL/FL training run, optimizing for various training goals such as fast convergence, fairness, and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a statically defined aggregation function is used in distributed learning and federated learning, then the system structure is simple and easy to implement, but the training speed and convergence efficiency are suboptimal
Solution Approach 1:
The patent applies the dynamics principle by transitioning from a static aggregation function to a dynamic one. The aggregation function is updated periodically during training based on runtime system state, allowing it to adapt to changing conditions. This resolves the contradiction by making the system more complex in structure but significantly improving training speed and convergence efficiency through adaptive optimization.
Solution Approach 2:
The patent implements feedback by using the system state (including training progress, resource utilization, and convergence metrics) to inform updates to the aggregation function. This closed-loop control mechanism allows the system to learn from its performance and automatically adjust the aggregation strategy, thereby improving productivity while managing complexity through intelligent automation.
2Productivity
If a statically defined aggregation function is used, then the implementation is straightforward, but resource consumption and training efficiency cannot be optimized
Solution Approach 1:
The patent applies self-service by enabling the aggregation function to automatically adjust itself based on system feedback without requiring manual intervention. The system monitors its own state and autonomously optimizes the aggregation strategy, improving training efficiency while maintaining ease of operation through automated self-optimization rather than complex manual tuning.
Solution Approach 2:
The patent changes the parameters of the aggregation function dynamically based on system state. By adjusting aggregation parameters (such as weighting factors or selection criteria) according to runtime conditions, the system optimizes training efficiency without requiring complete redesign of the implementation, thus balancing productivity improvement with ease of operation.
3Reliability
If the aggregation function is dynamically changed to optimize training goals, then convergence speed and fairness improve, but the system complexity increases
Solution Approach 1:
The patent uses dynamics to make the aggregation function adaptive rather than fixed. By allowing the aggregation function to change its behavior based on system state, the patent improves convergence reliability and fairness while managing complexity through structured adaptation rather than uncontrolled complexity.
Solution Approach 2:
The patent implements feedback loops where system performance metrics feed into aggregation function updates. This ensures that changes to the aggregation function are driven by actual system needs, improving reliability while keeping complexity manageable through purposeful, data-driven adjustments rather than arbitrary complexity.
Data Source
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AI summary
A framework for implementing reinforcement learning (RL)-based dynamic aggregation for distributed learning (DL) and federated learning (FL) is provided. In one set of embodiments, the framework includes an RL agent that interacts with the parameter server and clients of a DL/FL system and periodically receives two inputs from the system while the system is executing a training run: a "state" comprising information regarding the current runtime properties of the system and a "reward" comprising information pertaining to one or more training metrics to be optimized. In response to these inputs, the RL agent generates an "action" comprising information for modifying the parameter server's aggregation function in a manner that maximizes future cumulative rewards expected from the DL/FL system based on the state.