29 citations · 47 across the 4 of their papers we have counts for
5 papers
Distributed Learning as a Service: The Developer's Perspective
Tianyue Chu, Filippo Vannella, Dimitra Tsigkari +5
Application developers of distributed learning services face challenges that a typical federated learning loop does not address. Specifically, the model updates can still leak priv…
Data Heterogeneity and Forgotten Labels in Split Federated Learning
Joana Tirana, Dimitra Tsigkari, David Solans Noguero +1
In Split Federated Learning (SFL), the clients collaboratively train a model with the help of a server by splitting the model into two parts. Part-1 is trained locally at each clie…
Workflow Optimization for Parallel Split Learning
Joana Tirana, Dimitra Tsigkari, George Iosifidis +1
Split learning (SL) has been recently proposed as a way to enable resource-constrained devices to train multi-parameter neural networks (NNs) and participate in federated learning…
Quid pro Quo in Streaming Services: Algorithms for Cooperative Recommendations
Dimitra Tsigkari, George Iosifidis, Thrasyvoulos Spyropoulos
Recommendations are employed by Content Providers (CPs) of streaming services in order to boost user engagement and their revenues. Recent works suggest that nudging recommendation…
An approximation algorithm for joint caching and recommendations in cache networks
Dimitra Tsigkari, Thrasyvoulos Spyropoulos
Streaming platforms, like Netflix and YouTube, strive to offer high streaming quality (SQ), in terms of bitrate, delays, etc., to their users. Meanwhile, a significant share of con…