Adaptive Optimization Parameters in Federated Learning
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Solution Overview
Problem
Existing federated learning methods rely on manually designed optimization algorithms, which are laborious to select and often sub-optimal, leading to slow convergence of model parameters and increased communication costs due to the need for multiple rounds of training.
Innovation Solution
A parameterized optimization algorithm is used, where the optimization parameters are updated during each round of training using machine learning methods such as K-armed bandit algorithms, reinforcement learning agents, or pre-trained policies, allowing for adaptive optimization without manual selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manually designed optimization algorithms are used in federated learning, then the algorithm selection process is simple, but the convergence speed is slow and communication costs increase
Solution Approach 1:
The patent transforms the fixed optimization algorithm into a parameterized form where hyperparameters can be dynamically adjusted. By representing optimization algorithms as parameterized functions with learnable hyperparameters, the system can adapt convergence behavior during training without changing the underlying algorithm structure, thereby improving convergence speed while maintaining systematic control.
Solution Approach 2:
The patent implements self-service by enabling the optimization algorithm to automatically select and adjust its own hyperparameters through the learned policy. The system uses feedback from training progress to autonomously modify optimization parameters without manual intervention, allowing the algorithm to adapt to different federated learning scenarios and improve convergence automatically.
2Productivity
If manually selected optimization algorithms are used, then the system complexity is low, but the communication cost increases due to multiple training rounds
Solution Approach 1:
By enabling dynamic adjustment of optimization hyperparameters through the learned policy, the system can achieve faster convergence in fewer training rounds. This reduces the number of communication cycles between central server and edge devices, thereby lowering communication costs while improving training efficiency.
Solution Approach 2:
The patent incorporates feedback mechanisms where the performance and state of federated learning are continuously monitored and used to adjust optimization parameters. This closed-loop control allows the system to adapt to convergence patterns and reduce unnecessary communication rounds, optimizing the balance between training efficiency and communication cost.
3Productivity
If a parameterized optimization algorithm with adaptive parameters is used, then the convergence speed improves, but the device complexity increases
Solution Approach 1:
The patent creates a universal parameterized optimization framework that can represent multiple optimization algorithms through a single unified structure with adjustable hyperparameters. This multi-functional approach allows the same algorithmic framework to adapt to different scenarios by changing parameters rather than requiring separate algorithms, managing complexity while maintaining versatility.
Solution Approach 2:
The learned policy acts as an intermediary between the federated learning process and the optimization algorithm. Instead of directly modifying complex optimization logic, the policy serves as a mediator that adjusts hyperparameters based on learned patterns, simplifying the overall system architecture while enabling adaptive optimization behavior.
4Ease of operation
If machine learning methods are used to update optimization parameters, then manual selection is eliminated, but the computational overhead increases
Solution Approach 1:
The patent performs preliminary action by pre-training the policy model offline using historical federated learning data. This pre-computed knowledge is then applied during actual federated learning execution, eliminating the need for real-time complex computations and reducing computational overhead while maintaining the ability to provide adaptive parameter updates.
Data Source
AI summary
Methods and systems for federated learning using a parameterized optimization algorithm are described. A central server receives, from each of a plurality of user devices, a proximal map and feedback representing a current state of each user device. The server computes an update to optimization parameters of a parameterized optimization algorithm, using the received feedback. Model updates are computed for each user device, using the received proximal maps and the parameterized optimization algorithm having the updated optimization parameters. Each model update is transmitted to each respective client for updating the respective model.