We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets
and a scalable runtime to enable reproducible FL research. FedScale datasets encompass …

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that
enables in-situ model training and testing on edge data. Despite having the same end goals …

Cloud applications are increasingly distributing data across multiple regions and cloud
providers. Unfortunately, widearea bulk data transfers are often slow, bottlenecking …

Federated learning (FL) is an emerging machine learning (ML) paradigm that enables
heterogeneous edge devices to collaboratively train ML models without revealing their raw …

In this paper, we present the design and implementation of strongly consistent replication in
MongoDB. MongoDB provides linearizability and tolerates any minority of failures through a …

Many geo-distributed data analytics (GDA) systems have focused on the network
performance-bottleneck: inter-data center network bandwidth to improve performance …

Adaptability is critical for stream processing systems to ensure stable, low-latency, and high-
throughput processing of long-running queries. Such adaptability is particularly challenging …

The popularity of big data and AI has led to many optimizations at different layers of
distributed computation stacks. Despite–or perhaps, because of–its role as the narrow waist …

In this paper, we look into the problem of achieving efficient inter-datacenter AllReduce
operations for geo-distributed machine learning (Geo-DML). Compared with intra-datacenter …

Large-scale real-time analytics services continuously collect and analyze data from end-
user applications and devices distributed around the globe. Such analytics requires data to …