Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving

no code implementations • 8 Dec 2023 • Yinwei Dai, Rui Pan, Anand Iyer, Kai Li, Ravi Netravali

Machine learning (ML) inference platforms are tasked with balancing two competing goals: ensuring high throughput given many requests, and delivering low-latency responses to support interactive applications.

Auxo: Efficient Federated Learning via Scalable Client Clustering

no code implementations • 29 Oct 2022 • Jiachen Liu, Fan Lai, Yinwei Dai, Aditya Akella, Harsha Madhyastha, Mosharaf Chowdhury

In this paper, we explore an additional layer of complexity to mitigate such heterogeneity by grouping clients with statistically similar data distributions (cohorts).

Clustering Federated Learning

FedScale: Benchmarking Model and System Performance of Federated Learning at Scale

3 code implementations • 24 May 2021 • Fan Lai, Yinwei Dai, Sanjay S. Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha V. Madhyastha, Mosharaf Chowdhury

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

Benchmarking Federated Learning +6