Abstract With the Internet of Things (IoT) becoming part of our daily life and our environment,
we expect rapid growth in the number of connected devices. IoT is expected to connect …

Federated learning using fog computing can suffer from the dynamic behavior of some of the
participants in its training process, especially in Internet-of-Vehicles where vehicles are the …

The increasing popularity of applications with tight latency requirements has motivated
research on Edge Computing, which positions computing resources near data sources at …

In contemporary computing paradigms, the evolution from cloud computing to fog computing
and the recent emergence of federated-fog computing have introduced new challenges …

Federated learning is a distributed learning paradigm where multiple agents, each only with
access to local data, jointly learn a global model. There has recently been an explosion of …

Instability within fog federations is considered as a serious problem that degrades the
performance of the provided services. The latter may affect the service availability due to fog …

Fog computing is envisioned as a promising approach for supporting emerging computation-
intensive applications on capacity and battery constrained mobile Internet of Things (IoT) …

A primary concern in fog computing is how to efficiently allocate limited fog resources to
applications with diverse resource requirements. In fog computing, applications that consist …

Federated fog computing is an answer for horizontally upscaling fog resources to improve
the Quality of Service (QoS) of Internet of Things (IoT) applications. However, the dynamic …

Network delays cause disturbance and reduction in the Quality-of-Service (QoS) for Internet-
of-Things (IoT) while end-users are running critical real-time services. In parallel, federated …