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Coping with Data Heterogeneity in Federated Learning
Published:
Federated Learning (or, more generally, decentralized machine learning) is becoming increasingly important due to the geographically distributed nature of data. Data generated by IoT devices, video cameras, and mobile phones are often stored directly on the device or on nearby edge devices. It is infeasible to transfer all of this data to a centralized location for model training. Sending data over wide-area network (WAN) links is ofent slow and expensive. Futhermore, legal requirements and privacy regulations are becoming more prevalent, which restrict where data can be transferred and stored.
publications
Poster: Data-Aware Edge Sampling for Aggregate Query Approximation
Joel Wolfrath and Abhishek Chandra. 2020. Data-Aware Edge Sampling for Aggregate Query Approximation. In 2020 IEEE/ACM Symposium on Edge Computing (SEC 2020).
Best Poster Award
Accelerated Training via Device Similarity in Federated Learning
Yuanli Wang, Joel Wolfrath, Nikhil Sreekumar, Dhruv Kumar, Abhishek Chandra. 2021. Accelerated Training via Device Similarity in Federated Learning. In 4th International Workshop on Edge Systems, Analytics and Networking (EdgeSys 2021).
Towards WAN-Aware Join Sampling over Geo-Distributed Data
Dhruv Kumar, Joel Wolfrath, Abhishek Chandra, Ramesh Sitaraman. 2022. Towards WAN-Aware Join Sampling over Geo-Distributed Data. In 5th International Workshop on Edge Systems, Analytics and Networking (EdgeSys 2022).
HACCS: Heterogeneity-Aware Clustered Client Selection for Accelerated Federated Learning
Joel Wolfrath, Nikhil Sreekumar, Dhruv Kumar, Yuanli Wang, and Abhishek Chandra. 2022. HACCS: Heterogeneity-Aware Clustered Client Selection for Accelerated Federated Learning. 36th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2022).
Efficient Transmission and Reconstruction of Dependent Data Streams via Edge Sampling
Joel Wolfrath and Abhishek Chandra. 2022. Efficient Transmission and Reconstruction of Dependent Data Streams via Edge Sampling. 10th IEEE International Conference on Cloud Engineering (IC2E 2022).
Plexus: Optimizing Join Approximation for Geo-Distributed Data Analytics
Joel Wolfrath and Abhishek Chandra. 2023. Plexus: Optimizing Join Approximation for Geo-Distributed Data Analytics. 14th ACM Symposium on Cloud Computing (SoCC 2023).
Leveraging Multi-Modal Data for Efficient Edge Inference Serving
Joel Wolfrath, Anirudh Achanta, and Abhishek Chandra. 2024. Leveraging Multi-Modal Data for Efficient Edge Inference Serving. 24th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2024).