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FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification

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์ €์ž

Po-Hsien Yu, Yu-Syuan Tseng, Shao-Yi Chien

๐Ÿ’ก ๊ฐœ์š”

๋ณธ ๋…ผ๋ฌธ์€ ๊ฐœ์ธ์ •๋ณด ๋ณดํ˜ธ๊ฐ€ ์ค‘์š”ํ•œ ์‚ฌ๋žŒ ์žฌ์‹๋ณ„(person re-ID) ์ž‘์—…์—์„œ ํ†ต๊ณ„์  ์ด์งˆ์„ฑ๊ณผ ๋†’์€ ํ†ต์‹  ๋น„์šฉ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด FedKLPR์ด๋ผ๋Š” ๊ฒฝ๋Ÿ‰ ๋ฐ ํ†ต์‹  ํšจ์œจ์ ์ธ ์—ฐํ•ฉ ํ•™์Šต ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. KL-๋ฐœ์‚ฐ(KL-Divergence) ๊ธฐ๋ฐ˜ ํ•™์Šต๊ณผ ๋น„๊ตฌ์กฐํ™” ๊ฐ€์ง€์น˜๊ธฐ(unstructured pruning)๋ฅผ ๊ฒฐํ•ฉํ•œ FedKLPR์€ ๋ฐ์ดํ„ฐ ๋ถˆ๊ท ํ˜• ๋ฌธ์ œ๋ฅผ ์™„ํ™”ํ•˜๊ณ  ํ†ต์‹ ๋Ÿ‰์„ ํฌ๊ฒŒ ์ค„์ด๋ฉด์„œ๋„ ๊ฒฝ์Ÿ๋ ฅ ์žˆ๋Š” ์ •ํ™•๋„๋ฅผ ์œ ์ง€ํ•ฉ๋‹ˆ๋‹ค.

๐Ÿ”‘ ์‹œ์‚ฌ์  ๋ฐ ํ•œ๊ณ„

โ€ข
ํ†ต๊ณ„์  ์ด์งˆ์„ฑ ์™„ํ™” ๋ฐ ์•ˆ์ •์ ์ธ ์ˆ˜๋ ด: KL-๋ฐœ์‚ฐ ์ •๊ทœํ™” ์†์‹ค(KLL)๊ณผ KL-๋ฐœ์‚ฐ ์ง‘๊ณ„ ๊ฐ€์ค‘์น˜(KLAW)๋ฅผ ํ†ตํ•ด ๋น„-IID ๋ฐ์ดํ„ฐ ํ™˜๊ฒฝ์—์„œ๋„ ๋ชจ๋ธ ์ˆ˜๋ ด ์•ˆ์ •์„ฑ์„ ๋†’์ด๊ณ  ํ†ต๊ณ„์  ์ด์งˆ์„ฑ์„ ํšจ๊ณผ์ ์œผ๋กœ ์™„ํ™”ํ•ฉ๋‹ˆ๋‹ค.
โ€ข
ํ†ต์‹  ํšจ์œจ์„ฑ ๋ฐ ๋ชจ๋ธ ์••์ถ•: ๋น„๊ตฌ์กฐํ™” ๊ฐ€์ง€์น˜๊ธฐ ์ „๋žต๊ณผ ๊ฐ€์ง€์น˜๊ธฐ ๋น„์œจ ์ง‘๊ณ„ ๊ฐ€์ค‘์น˜(PRAW)๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ชจ๋ธ ํฌ๊ธฐ๋ฅผ ์ค„์ด๊ณ  ํ†ต์‹  ์˜ค๋ฒ„ํ—ค๋“œ๋ฅผ ๊ฐ์†Œ์‹œํ‚ค๋ฉฐ, KL-๋ฐœ์‚ฐ-๊ฐ€์ง€์น˜๊ธฐ ๊ฐ€์ค‘ ์ง‘๊ณ„(KLPWA)๋Š” ๊ฐ€์ง€์น˜๊ธฐ๋œ ๋กœ์ปฌ ๋ชจ๋ธ์˜ ํšจ๊ณผ์ ์ธ ์ง‘๊ณ„๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค.
โ€ข
๋ฐ˜๋ณต์  ์••์ถ•์—์„œ์˜ ์ •ํ™•๋„ ์œ ์ง€: ๊ต์ฐจ ๋ผ์šด๋“œ ๋ณต๊ตฌ(CRR) ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ํ†ตํ•ด ๊ณผ๋„ํ•œ ๊ฐ€์ง€์น˜๊ธฐ๋ฅผ ๋ฐฉ์ง€ํ•˜๊ณ  ๋ฐ˜๋ณต์ ์ธ ์••์ถ• ๊ณผ์ •์—์„œ ๋ชจ๋ธ ์ •ํ™•๋„๋ฅผ ๋ณด์กดํ•ฉ๋‹ˆ๋‹ค.
โ€ข
ํ•œ๊ณ„์ : ์ œ์•ˆ๋œ ํ”„๋ ˆ์ž„์›Œํฌ์˜ ๋ณต์žก์„ฑ์ด ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ๊ตฌํ˜„ ๋ฐ ์ตœ์ ํ™”์— ์ถ”๊ฐ€์ ์ธ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋งค์šฐ ๊ทนํ•œ์˜ ๋น„-IID ํ™˜๊ฒฝ์—์„œ์˜ ์„ฑ๋Šฅ์— ๋Œ€ํ•œ ์ถ”๊ฐ€์ ์ธ ๊ฒ€์ฆ์ด ํ•„์š”ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
๐Ÿ‘
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