Federated Multi-Task Learning
Abstrak
Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task learning is naturally suited to handle the statistical challenges of this setting, and propose a novel systems-aware optimization method, MOCHA, that is robust to practical systems issues. Our method and theory for the first time consider issues of high communication cost, stragglers, and fault tolerance for distributed multi-task learning. The resulting method achieves significant speedups compared to alternatives in the federated setting, as we demonstrate through simulations on real-world federated datasets.
Topik & Kata Kunci
Penulis (4)
Virginia Smith
Chao-Kai Chiang
Maziar Sanjabi
Ameet Talwalkar
Akses Cepat
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Cek di sumber asli →- Tahun Terbit
- 2017
- Bahasa
- en
- Total Sitasi
- 2080×
- Sumber Database
- Semantic Scholar
- Akses
- Open Access ✓