Federated learning (FL) provides a privacy-preserving approach for
collaborative training of machine learning models. Given the potential data
heterogeneity, it is crucial to select appropriate collaborators for each FL
participant (FL-PT) based on data complementarity. Recent studies have
addressed this challenge. Similarly, it is imperative to consider the
inter-individual relationships among FL-PTs where some FL-PTs engage in
competition. Although FL literature has acknowledged the significance of this
scenario, practical methods for establishing FL ecosystems remain largely
unexplored. In this paper, we extend a principle from the balance theory,
namely ``the friend of my enemy is my enemy'', to ensure the absence of
conflicting interests within an FL ecosystem. The extended principle and the
resulting problem are formulated via graph theory and integer linear
programming. A polynomial-time algorithm is proposed to determine the
collaborators of each FL-PT. The solution guarantees high scalability, allowing
even competing FL-PTs to smoothly join the ecosystem without conflict of
interest. The proposed framework jointly considers competition and data
heterogeneity. Extensive experiments on real-world and synthetic data
demonstrate its efficacy compared to five alternative approaches, and its
ability to establish efficient collaboration networks among FL-PTs.

在这篇论文中，我们利用图论和整数线性规划，通过扩展平衡理论中的一个原则来确保在联邦学习生态系统中不存在利益冲突，从而确定每个 FL-PT 的合作者，解决了数据异质性和竞争性之间的冲突，并通过与其他五种方法的比较进行了实证研究来证明其有效性。

FedCompetitors：竞争参与者的协调协作联邦学习

FedCompetitors: Harmonious Collaboration in Federated Learning with  Competing Participants

Federated learning (FL) has been proposed as a privacy-preserving approach in
distributed machine learning. A federated learning architecture consists of a
central server and a number of clients that have access to private, potentially
sensitive data. Clients are able to keep their data in their local machines and
only share their locally trained model's parameters with a central server that
manages the collaborative learning process. FL has delivered promising results
in real-life scenarios, such as healthcare, energy, and finance. However, when
the number of participating clients is large, the overhead of managing the
clients slows down the learning. Thus, client selection has been introduced as
a strategy to limit the number of communicating parties at every step of the
process. Since the early naïve random selection of clients, several client
selection methods have been proposed in the literature. Unfortunately, given
that this is an emergent field, there is a lack of a taxonomy of client
selection methods, making it hard to compare approaches. In this paper, we
propose a taxonomy of client selection in Federated Learning that enables us to
shed light on current progress in the field and identify potential areas of
future research in this promising area of machine learning.

本文提出了一种客户端选择的分类法，以便比较不同的方法，旨在在机器学习这个有前途的领域中，为未来的研究提供方向。

联邦学习中客户选择前沿的快照

A Snapshot of the Frontiers of Client Selection in Federated Learning

State-of-the-art extractive multi-document summarization systems are usually
designed without any concern about privacy issues, meaning that all documents
are open to third parties. In this paper we propose a privacy-preserving
approach to multi-document summarization. Our approach enables other parties to
obtain summaries without learning anything else about the original documents'
content. We use a hashing scheme known as Secure Binary Embeddings to convert
documents representation containing key phrases and bag-of-words into bit
strings, allowing the computation of approximate distances, instead of exact
ones. Our experiments indicate that our system yields similar results to its
non-private counterpart on standard multi-document evaluation datasets.

本文提出了一种保护隐私的方法，使用安全二进制嵌入哈希方案将包含关键短语和词袋的文档表示转换为位串，使其他方可以获取摘要而不学习原始文档内容，实验结果表明我们的系统在标准多文档评估数据集上与非隐私匹配的系统产生相似的结果。