Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2025: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2024: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2023: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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Outline of Research at the Start |
The booming mobile deep learning applications that enable personalized experiences are challenging the current computing and communication network architectures. The state-of-the-art federated learning (FL) and split learning (SL) based solutions, however, are constrained by computational resources and communication resources respectively. To this end, in this research, we propose an efficient split in-network learning approach for resource constrained wireless networks, which amalgamates the FL and SL techniques and eliminates their inherent drawbacks.
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