Dual-Mode Federated Learning for Client Idle Time and Cost Control
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Solution Overview
Problem
Existing federated learning approaches face ambiguity in the relative costs of synchronous and asynchronous training methods, leading to inefficient resource utilization, particularly in constrained budget scenarios like cloud computing and mobile devices, where extended runtime impacts user experience.
Innovation Solution
A method to determine individual training times for trainer clients, compute sync and async metrics for convergence, and provide a comparison to dynamically switch between synchronous and asynchronous modes based on cost and time metrics, using algorithms like FedAvg and FedBuff.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If synchronous federated learning is used, then model convergence is achieved with coordinated aggregation, but clients spend large portion of time idling while waiting for other clients
Solution Approach 1:
The system dynamically switches between synchronous and asynchronous federated learning modes based on real-time performance metrics and system conditions. The server can adjust the aggregation mode for different client groups or at different training stages, making the system adaptable to varying client availability and performance characteristics rather than being locked into a single mode.
2Productivity
If asynchronous federated learning is used, then training speed is improved and clients operate independently, but the relative cost remains ambiguous and resource utilization becomes inefficient
Solution Approach 1:
The system implements comprehensive feedback mechanisms that track multiple metrics including training speed, resource utilization, convergence quality, and computational costs. This feedback is used to continuously evaluate the performance of asynchronous versus synchronous modes and inform dynamic switching decisions, making the cost-benefit analysis transparent and actionable.
Solution Approach 2:
The system changes operational parameters dynamically by adjusting the aggregation mode (synchronous/asynchronous), client selection strategies, and hyperparameters based on observed performance and cost metrics. This allows the system to optimize for different objectives at different times, such as prioritizing speed when resources are abundant or minimizing costs when budget is constrained.
3Ease of operation
If asynchronous federated learning is used, then clients continue training operations independent of other participants, but resource utilization becomes inefficient in constrained budget scenarios
Solution Approach 1:
The system segments the federated learning process into different phases or client groups that can operate in different modes simultaneously. For example, well-resourced clients may operate asynchronously while resource-constrained clients use synchronous mode, or the system may alternate between modes for different rounds of training based on overall system state.
Data Source
AI summary
In one embodiment, an illustrative method herein comprises: determining, by a device, individual times for a set of trainer clients of a federated learning system to complete a round of machine learning model training and communication; computing, by the device, a sync time metric and a sync cost metric of a synchronous federated learning mode to reach model convergence based in part on the individual times; computing, by the device, an async time metric and an async cost metric of an asynchronous federated learning mode to reach model convergence based in part on the individual times and an asynchronous concurrency; and providing, by the device, a comparison between the synchronous federated learning mode and the asynchronous federated learning mode for the federated learning system according to the sync time metric, the sync cost metric, the async time metric, and the async cost metric.