Straggler Device Identification in Federated Learning
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Solution Overview
Problem
In federated learning, straggler devices that consistently fail to transmit local models within a threshold time period hinder the aggregation process, leading to delays and inefficiencies in updating the aggregated model.
Innovation Solution
An apparatus and method to identify straggler devices by tracking transmission delays and suspending model transmission to them, resuming when specific resumption criteria are met, such as improved network conditions or computational power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system waits for all devices to complete local model training before aggregation, then all devices can contribute their local models, but the aggregation process experiences delays due to straggler devices
Solution Approach 1:
The patent extracts straggler devices from the aggregation process by identifying them based on counter thresholds and temporarily excluding them from receiving aggregated models. This allows the aggregation process to proceed without being blocked by devices that are consistently slow to complete training, thereby reducing aggregation delays while maintaining the contribution of timely devices.
Solution Approach 2:
The patent implements dynamic management of device participation in federated learning by using counters that track the number of times a device fails to complete training within the time window. Devices can be temporarily suspended and later reinstated based on their performance, creating a dynamic rather than static participation model that adapts to device performance variations.
2Productivity
If the system suspends transmission of aggregated models to straggler devices, then aggregation efficiency improves, but the straggler devices are excluded from the learning process
Solution Approach 1:
The patent implements periodic re-evaluation of straggler devices through a reinstatement mechanism. Devices that were temporarily suspended can have their counters reset and be reinstated into the federated learning process after a certain number of iterations or under specific conditions, ensuring periodic opportunities for participation while maintaining overall aggregation efficiency.
Solution Approach 2:
The patent changes the participation status parameter of straggler devices dynamically based on counter values. By adjusting the counter threshold and time window parameters, the system can control the degree of exclusion and reinstatement, allowing flexible management of device participation to balance efficiency and inclusivity.
3Stability of the object's composition
If the system uses a strict time threshold for model submission, then training consistency is maintained, but devices with computational constraints are identified as stragglers
Solution Approach 1:
The patent applies preliminary action by setting up counter tracking and threshold-based identification mechanisms before the federated learning process begins. This allows the system to proactively identify potential stragglers and apply consistent suspension/reinstatement rules, maintaining training consistency while accommodating device variations through pre-established adaptive mechanisms.
Data Source
AI summary
This specification discloses an apparatus, method and computer program. The method may comprise identifying one or more straggler devices among a plurality of devices, suspending transmission of an aggregated model to the one or more straggler devices for local model training, and resuming transmission of the aggregated model to at least one of the one or more straggler devices if the at least one straggler device meets one or more resumption criteria at a subsequent time.