隨著人工智慧的快速發展，各行業對人工智慧的表現要求也逐漸提高，機器學習模型需要做出更準確的預測和推薦，模型訓練所需要的數據量和數據多樣性勢必得大幅提升；礙於大多數領域所擁有的數據量有限，企業間又因為競爭關係而無法輕易交換數據。近年來人民也逐漸意識到數據隱私的重要性，使得數據的收集難度大幅增加。在這樣的挑戰下，聯盟式學習成為解決這些難題的一大技術。
聯盟式學習的分散式訓練架構，讓參與訓練的設備可以在不上傳私人數據的情況下進行訓練，每個設備各自利用自己的私人數據來共同訓練一個共享模型，同時解決數據收集和隱私安全的問題。近年來聯盟式學習也隨著快速發展而逐漸發現其問題所在，除了設備間的能力不相等造成模型無法很好泛化，每個設備的數據分布差異也造成模型表現效能低落。實現更個人化的聯盟式學習也成為未來研究的一大方向。
本研究將能克服模型差異的雙向蒸餾加入到聯盟式學習的設置中，因應訓練設備之間的模型多樣性。先將相似特性的模型進行分群。每個群集會有一個Prototype Model (原型模型)。在Prototype Model上訓練出一個規模更大的Meta Model (全域模型)，透過雙向蒸餾的方式提升彼此模型間的效能，也進而提升每個設備端模型效能的表現。經過實驗證明，透過全域模型蒸餾而得到的新Prototype Model，其準確率皆比原模型來的高。

In recent years, the demand for artificial intelligence (AI) has been steadily increasing across various industries. Consequently, there is a significant need for larger datasets and greater diversity to train AI models effectively. However, the reality is that most fields have limited access to data, and businesses face challenges in exchanging information due to competitive relationships. Moreover, the growing awareness of data privacy among people has significantly complicated data collection efforts. As a result, the inability to acquire sufficient data has posed a challenge in training high-performing models. In response to these difficulties, Federated learning has emerged as a prominent solution to address these issues.
In the framework of Federated learning, every device contributes to the collaborative training of a shared model using its own data. This approach eliminates the necessity of uploading private data, thereby addressing concerns related to data collection and privacy security. However, as federated learning has rapidly progressed in recent years, its associated challenges have also come to light. One such challenge is the unequal capabilities among devices, which hinder the easy generalization of models. Additionally, the performance of the models is influenced by variations in data distribution across different devices. As a result, achieving more personalized Federated learning has emerged as a key research direction for the future.
This study incorporates the bidirectional distillation technique, which addresses
model differences, into the framework of Federated learning. Firstly, models with similar characteristics are grouped together. Each group has a Prototype Model,
and the parameters transmitted by client devices are aggregated. Subsequently,
each Prototype Model is trained using unlabeled data. A larger Meta Model is then
trained based on these Prototype Models. By employing bidirectional distillation,
the performance of the Prototype Models is enhanced, leading to improved performance of individual models on each device.

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