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Found 5 papers, 1 papers with code
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without touching private data owned by individual clients.
Optimal Rate Adaption in Federated Learning with Compressed Communications
Federated Learning (FL) incurs high communication overhead, which can be greatly alleviated by compression for model updates.
Federated Unlearning
In this paper, we take the first step to fill this gap by presenting FedEraser, the first federated unlearning methodology that can eliminate the influence of a federated client's data on the global FL model while significantly reducing the time used for constructing the unlearned FL model. The basic idea of FedEraser is to trade the central server's storage for unlearned model's construction time, where FedEraser reconstructs the unlearned model by leveraging the historical parameter updates of federated clients that have been retained at the central server during the training process of FL.
Personalized Cross-Silo Federated Learning on Non-IID Data
Non-IID data present a tough challenge for federated learning.
Forecasting Popularity of Videos using Social Media
This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media.
