Federated Learning enables mobile users to collaboratively learn a global prediction model
by aggregating their individual updates without sharing the privacy-sensitive data. As mobile …

CMFL: Mitigating Communication Overhead for Federated Learning | CiNii Research CiNii
国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細へ移動 検索フォームへ移動 論文・データを …

Federated Learning enables mobile users to collaboratively learn a global prediction model
by aggregating their individual updates without sharing the privacy-sensitive data. As mobile …

Federated Learning enables mobile users to collaboratively learn a global prediction model
by aggregating their individual updates without sharing the privacy-sensitive data. As mobile …

Federated Learning enables mobile users to collaboratively learn a global prediction model
by aggregating their individual updates without sharing the privacy-sensitive data. As mobile …

Federated Learning enables mobile users to collaboratively learn a global prediction model
by aggregating their individual updates without sharing the privacy-sensitive data. As mobile …

Federated Learning enables mobile users to collaboratively learn a global prediction model
by aggregating their individual updates without sharing the privacy-sensitive data. As mobile …

Federated Learning enables mobile users to collaboratively learn a global prediction model
by aggregating their individual updates without sharing the privacy-sensitive data. As mobile …