Federated Knowledge Graph Embedding (FKGE) has recently garnered considerable
interest due to its capacity to extract expressive representations from
distributed knowledge graphs, while concurrently safeguarding the privacy of
individual clients. Existing FKGE methods typically harness the arithmetic mean
of entity embeddings from all clients as the global supplementary knowledge,
and learn a replica of global consensus entities embeddings for each client.
However, these methods usually neglect the inherent semantic disparities among
distinct clients. This oversight not only results in the globally shared
complementary knowledge being inundated with too much noise when tailored to a
specific client, but also instigates a discrepancy between local and global
optimization objectives. Consequently, the quality of the learned embeddings is
compromised. To address this, we propose Personalized Federated knowledge graph
Embedding with client-wise relation Graph (PFedEG), a novel approach that
employs a client-wise relation graph to learn personalized embeddings by
discerning the semantic relevance of embeddings from other clients.
Specifically, PFedEG learns personalized supplementary knowledge for each
client by amalgamating entity embedding from its neighboring clients based on
their "affinity" on the client-wise relation graph. Each client then conducts
personalized embedding learning based on its local triples and personalized
supplementary knowledge. We conduct extensive experiments on four benchmark
datasets to evaluate our method against state-of-the-art models and results
demonstrate the superiority of our method.

使用客户关系图进行个性化联邦知识图嵌入的方法（PFedEG），通过识别来自其他客户端的嵌入的语义关联性，为每个客户端学习个性化的嵌入，从而解决了现有 FKGE 方法中的全局共享补充知识被噪音淹没和局部与全局优化目标不一致的问题。

个性化联邦知识图嵌入与客户关系图

Personalized Federated Knowledge Graph Embedding with Client-Wise  Relation Graph

The video composition task aims to integrate specified foregrounds and
backgrounds from different videos into a harmonious composite. Current
approaches, predominantly trained on videos with adjusted foreground color and
lighting, struggle to address deep semantic disparities beyond superficial
adjustments, such as domain gaps. Therefore, we propose a training-free
pipeline employing a pre-trained diffusion model imbued with semantic prior
knowledge, which can process composite videos with broader semantic
disparities. Specifically, we process the video frames in a cascading manner
and handle each frame in two processes with the diffusion model. In the
inversion process, we propose Balanced Partial Inversion to obtain generation
initial points that balance reversibility and modifiability. Then, in the
generation process, we further propose Inter-Frame Augmented attention to
augment foreground continuity across frames. Experimental results reveal that
our pipeline successfully ensures the visual harmony and inter-frame coherence
of the outputs, demonstrating efficacy in managing broader semantic
disparities.

通过使用一个预训练的扩散模型，处理具有更大语义差异的复合视频，我们的研究提出了一个无需训练的流程，以确保输出的视觉协调性和跨帧连贯性。