Across regions and organizational models, data center operators are facing a set of increasingly familiar trends. Infrastructure is becoming more complex, workloads more demanding and timelines more ...

Abstract: In federated learning, non-independently and non-identically distributed heterogeneous data on the clients can limit both the convergence speed and model utility of federated learning, and ...

Abstract: The RLS and RLS-DCD algorithms are commonly used for adaptive filtering applications due to their fast convergence even under correlated inputs. RLS-DCD is a numerically robust and easy to ...

Data and infrastructure have been stubborn components of AI implementations, hindering the training of models for targeted use cases and creating bottlenecks for in-demand services. Hyperscalers are ...