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Chapter
Local Training and Scalability of Federated Learning Systems
In this chapter, we delve deeper into the systems aspects of Federated Learning. We focus on the two main parts of FL—the participating devices (parties) and the aggregator’s scalability. First, we discuss the...
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Chapter
Systems Bias in Federated Learning
Data parties typically vary significantly in data quality, hardware resources, and stability, which results in challenges such as increased training times, higher resource costs, sub-par model performance and ...
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Chapter
Introduction to Federated Learning Systems
In this chapter, we introduce federated learning from a systems perspective. We go into the details of the different federated learning scenarios that have different system design considerations. We first intr...
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Chapter
Straggler Management
For this chapter, we elaborate on one of the most common challenge in Federated Learning—stragglers. The chapters “Local Training and Scalability of Federated Learning Systems“ and “Introduction to Federated Lear...
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Chapter and Conference Paper
Towards Decentralized Deep Learning with Differential Privacy
In distributed machine learning, while a great deal of attention has been paid on centralized systems that include a central parameter server, decentralized systems have not been fully explored. Decentralized ...
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Chapter and Conference Paper
Randomized Benchmarking of Quantum Gates on a GPU
While quantum computing has shown great promise in the field of computer science, a lack of actual practical quantum hardware means that mainstream research must rely on simulations. As such, a wide number of ...
