Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

In a federated learning system, it is often the case that the more clients it involves, the less
increment of the outcome it achieves. It is thus essential to design a client selection strategy …

Abstract Provisioning In-band Network Telemetry as a service for INT-based and INT follow-
up applications in an online manner suffers multiple challenges, including control decisions …

Hierarchical federated learning (HFL) improves the scalability and efficiency of traditional
federated learning (FL) by incorporating a hierarchical topology into the FL framework. In a …

In-band Network Telemetry (INT) is proposed to detect networks via injecting specific probes
to collect the hop-by-hop metadata within programmable switches. But there exist multiple …

Abstract The advent of Computing Power Network (CPN) has opened up vast opportunities
for machine learning inference, yet the challenge of reducing high operational cost due to …

In-band Network Telemetry (INT) is a widely used monitoring framework in modern large-
scale networks. It provides packet-level visibility into network conditions by inserting …

Performing machine learning inference at the network edge, named Edge Inference,
showing benefits like low latency, reduced data traffic, and improved user privacy, has …

Delayed Gradient Averaging (DGA) has gained massive attention for improving the training
efficiency of Federated Learning (FL) at edge networks, by allowing local computation in …

Federated learning is a powerful distributed machine learning paradigm for feature
aggregation and learning from multiple heterogenous edge devices, due to its ability to keep …