302 citations · 695 across the 16 of their papers we have counts for
5 papers · 1 filter
Wukong: A Scalable and Locality-Enhanced Framework for Serverless Parallel Computing
Benjamin Carver, Jingyuan Zhang, Ao Wang +3
Serverless computing is increasingly being used for parallel computing, which have traditionally been implemented as stateful applications. Executing complex, burst-parallel, direc…
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers
Zheng Chai, Yujing Chen, Ali Anwar +3
Federated learning (FL) involves training a model over massive distributed devices, while keeping the training data localized. This form of collaborative learning exposes new trade…
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. How…
InfiniCache: Exploiting Ephemeral Serverless Functions to Build a Cost-Effective Memory Cache
Ao Wang, Jingyuan Zhang, Xiaolong Ma +6
Internet-scale web applications are becoming increasingly storage-intensive and rely heavily on in-memory object caching to attain required I/O performance. We argue that the emerg…
TiFL: A Tier-based Federated Learning System
Zheng Chai, Ahsan Ali, Syed Zawad +7
Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that ex…