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FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models

1. 연구 배경 및 문제 정의 데이터 중복 : 대규모 텍스트 코퍼스는 동일 문장·구절이 여러 클라이언트에 중복 저장되는 경우가 빈번해, 모델이 과적합하거나 프라이버시 위험이 증가한다. 연합 학습(Federated Learning, FL) : 데이터가 로컬에 머무르기 때문에 중앙 서버가 전체 데이터를 직접 확인할 수 없어, 기존의 중앙 집중형 중복 제거 가 적용 불가능하거나 신뢰할 수 없는 제3자에 의존하게 된다. 2. 기존 접근법의 한계 | 접근법 | 중복 처리 방식 | 프라이버시 위험 | 정보 손실 | 효율성 | | | | | |

FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models

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