Decentralized learning (DL) has gained prominence for its potential benefits
in terms of scalability, privacy, and fault tolerance. It consists of many
nodes that coordinate without a central server and exchange millions of
parameters in the inherently iterative process of machine learning (ML)
training. In addition, these nodes are connected in complex and potentially
dynamic topologies. Assessing the intricate dynamics of such networks is
clearly not an easy task. Often in literature, researchers resort to simulated
environments that do not scale and fail to capture practical and crucial
behaviors, including the ones associated to parallelism, data transfer, network
delays, and wall-clock time. In this paper, we propose DecentralizePy, a
distributed framework for decentralized ML, which allows for the emulation of
large-scale learning networks in arbitrary topologies. We demonstrate the
capabilities of DecentralizePy by deploying techniques such as sparsification
and secure aggregation on top of several topologies, including dynamic networks
with more than one thousand nodes.

本文提出了一个名为 DecentralizePy 的分布式框架，用于在任意拓扑结构下仿真大规模的去中心化学习网络，展示了在不同拓扑结构及节点数量下的去中心化学习所需的技术，包括剪枝和安全聚合。

使用 DecentralizePy 轻松实现去中心化学习

Decentralized Learning Made Easy with DecentralizePy

Multi-agent reinforcement learning for incomplete information environments
has attracted extensive attention from researchers. However, due to the slow
sample collection and poor sample exploration, there are still some problems in
multi-agent reinforcement learning, such as unstable model iteration and low
training efficiency. Moreover, most of the existing distributed framework are
proposed for single-agent reinforcement learning and not suitable for
multi-agent. In this paper, we design an distributed MARL framework based on
the actor-work-learner architecture. In this framework, multiple asynchronous
environment interaction modules can be deployed simultaneously, which greatly
improves the sample collection speed and sample diversity. Meanwhile, to make
full use of computing resources, we decouple the model iteration from
environment interaction, and thus accelerate the policy iteration. Finally, we
verified the effectiveness of propose framework in MaCA military simulation
environment and the SMAC 3D realtime strategy gaming environment with
imcomplete information characteristics.

本研究提出了一种基于 actor-work-learner 架构的分布式 MARL 框架，在 MaCA 军事模拟环境和 SMAC 3D 实时战略游戏环境中验证了其在多智能体强化学习性能提升方面的有效性。

协作多智能体强化学习的高效分布式框架

Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning

In this paper, we contribute a new million-scale face benchmark containing
noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M
faces (WebFace42M) training data, as well as an elaborately designed
time-constrained evaluation protocol. Firstly, we collect 4M name list and
download 260M faces from the Internet. Then, a Cleaning Automatically utilizing
Self-Training (CAST) pipeline is devised to purify the tremendous WebFace260M,
which is efficient and scalable. To the best of our knowledge, the cleaned
WebFace42M is the largest public face recognition training set and we expect to
close the data gap between academia and industry. Referring to practical
scenarios, Face Recognition Under Inference Time conStraint (FRUITS) protocol
and a test set are constructed to comprehensively evaluate face matchers.
Equipped with this benchmark, we delve into million-scale face recognition
problems. A distributed framework is developed to train face recognition models
efficiently without tampering with the performance. Empowered by WebFace42M, we
reduce relative 40% failure rate on the challenging IJB-C set, and ranks the
3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior
performance compared with public training set. Furthermore, comprehensive
baselines are established on our rich-attribute test set under
FRUITS-100ms/500ms/1000ms protocol, including MobileNet, EfficientNet,
AttentionNet, ResNet, SENet, ResNeXt and RegNet families. Benchmark website is
this https URL

该研究创建了一个新的百万级人脸基准数据集，包含清理后的面部图像，以及设计了一种时间约束的评估协议，建立了分布式框架以有效地训练人脸识别模型，并在 FRUITS 协议下建立了广泛的基准线。