Vanilla federated learning does not support learning in an online
environment, learning a personalized model on each client, and learning in a
decentralized setting. There are existing methods extending federated learning
in each of the three aspects. However, some important applications on
enterprise edge servers (e.g. online item recommendation at global scale)
involve the three aspects at the same time. Therefore, we propose a new
learning setting \textit{Decentralized Personalized Online Federated Learning}
that considers all the three aspects at the same time.
In this new setting for learning, the first technical challenge is how to
aggregate the shared model parameters from neighboring clients to obtain a
personalized local model with good performance on each client. We propose to
directly learn an aggregation by optimizing the performance of the local model
with respect to the aggregation weights. This not only improves personalization
of each local model but also helps the local model adapting to potential data
shift by intelligently incorporating the right amount of information from its
neighbors. The second challenge is how to select the neighbors for each client.
We propose a peer selection method based on the learned aggregation weights
enabling each client to select the most helpful neighbors and reduce
communication cost at the same time. We verify the effectiveness and robustness
of our proposed method on three real-world item recommendation datasets and one
air quality prediction dataset.

我们提出了一个新的去中心化个性化在线联邦学习设置，用于在在线环境中学习，在每个客户端上学习个性化模型，并在去中心化环境中学习。我们解决了两个技术挑战：如何通过优化本地模型的性能来聚合来自邻近客户端的共享模型参数，并通过学习的聚合权重选择最有帮助的邻居减少通信成本。

分布式个性化在线联邦学习

Decentralized Personalized Online Federated Learning

In peer selection agents must choose a subset of themselves for an award or a
prize. As agents are self-interested, we want to design algorithms that are
impartial, so that an individual agent cannot affect their own chance of being
selected. This problem has broad application in resource allocation and
mechanism design and has received substantial attention in the artificial
intelligence literature. Here, we present a novel algorithm for impartial peer
selection, PeerNomination, and provide a theoretical analysis of its accuracy.
Our algorithm possesses various desirable features. In particular, it does not
require an explicit partitioning of the agents, as previous algorithms in the
literature. We show empirically that it achieves higher accuracy than the
exiting algorithms over several metrics.

该论文介绍了一种新的算法 - PeerNomination，用于提高同行评议的公正性，避免个体代理的干扰，适用于资源分配、机制设计等领域，并在多个度量标准下在实证研究中取得更高的准确性。

同儕提名：放寬精確度以提高同儕選擇的準確性

PeerNomination: Relaxing Exactness for Increased Accuracy in Peer  Selection

Peer reviews, evaluations, and selections are a fundamental aspect of modern
science. Funding bodies the world over employ experts to review and select the
best proposals from those submitted for funding. The problem of peer selection,
however, is much more general: a professional society may want to give a subset
of its members awards based on the opinions of all members; an instructor for a
Massive Open Online Course (MOOC) or an online course may want to crowdsource
grading; or a marketing company may select ideas from group brainstorming
sessions based on peer evaluation.
We make three fundamental contributions to the study of peer selection, a
specific type of group decision-making problem, studied in computer science,
economics, and political science. First, we propose a novel mechanism that is
strategyproof, i.e., agents cannot benefit by reporting insincere valuations.
Second, we demonstrate the effectiveness of our mechanism by a comprehensive
simulation-based comparison with a suite of mechanisms found in the literature.
Finally, our mechanism employs a randomized rounding technique that is of
independent interest, as it solves the apportionment problem that arises in
various settings where discrete resources such as parliamentary representation
slots need to be divided proportionally.

针对同行评议在科学、社会和商业领域的广泛应用，本研究提出了一种策略诚实、有效的同行选择机制，并通过模拟比较和随机取整技术解决了资源分配等相关问题。