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Tighter Regret Analysis and Optimization of Online Federated Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 19, 2023
Summary
This study introduces OFedIQ, a communication-efficient method for online federated learning (OFL). It significantly reduces communication costs by 99% while maintaining model performance in distributed streaming data scenarios.
Area of Science:
- Machine Learning
- Distributed Systems
- Optimization
Background:
- Federated learning (FL) typically assumes offline data, but real-world applications require online processing of streaming data.
- Online FL (OFL) addresses this by learning sequential models from distributed data streams to minimize cumulative regret.
- The standard FedOGD method in OFL is communication-intensive.
Purpose of the Study:
- To develop a communication-efficient online federated learning (OFL) method.
- To derive a regret bound that accounts for data heterogeneity and communication efficiency techniques.
- To optimize parameters for improved performance and reduced communication overhead.
Main Methods:
- Introduced OFedIQ, a communication-efficient OFL algorithm.
- Employed intermittent transmission (client subsampling, periodic transmission) and gradient quantization.
- Derived a novel regret bound considering data heterogeneity and communication efficiency.
Main Results:
- OFedIQ achieves asymptotic performance comparable to FedOGD.
- Demonstrated a 99% reduction in communication costs.
- Validated effectiveness on real-world datasets across various online ML tasks.
Conclusions:
- OFedIQ offers a practical and efficient solution for online federated learning.
- The proposed method balances model performance with significant communication savings.
- Effective for distributed streaming data scenarios requiring real-time predictions.
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