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Description
Abstract
The recently awarded grant proposal, entitled “CAREER: Integrated and end-to-end machine learning pipeline
for edge-enabled IoT systems: a resource-aware and QoS-aware perspective” (Federal Award ID Number:
2340075), proposed an integrated resource-aware and QoS-aware data pre-processing and model training
system that can automatically optimize the system’s performance by dynamically balancing the allocation of
resources between data pre-processing and model training. Although data pre-processing and model training
have been separately studied in the literature, none of the previous work consider a wholistic and integrated
system design for such problem. Combining data pre-processing with model training enables adaptable
preprocessing techniques tailored to specific data characteristics and model needs. This approach
accommodates various sensor data conditions and optimizes data representation for precise predictions.
Unlike prior studies, we optimize both data pre-processing (including cleaning and reduction) and model
training concurrently, mindful of network constraints. This integration of decision-making processes creates a
feedback loop, enhancing both facets and resulting in improved data representation alignment and system
performance in many resources constrained applications such as Internet-of-Things (IoT) applications. This
research tackles challenges in distributed data analytics, sparse resource management, handling unlabeled
and noisy data and addressing device failures. Techniques such as network coding, coded computing,
reinforcement learning, compression, and resource management are employed to mitigate these challenges
effectively.
Status | Active |
---|---|
Effective start/end date | 3/1/24 → 2/28/29 |
Funding
- National Science Foundation
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Projects
- 1 Active