While centralized servers pose a risk of being a single point of failure,
decentralized approaches like blockchain offer a compelling solution by
implementing a consensus mechanism among multiple entities. Merging distributed
computing with cryptographic techniques, decentralized technologies introduce a
novel computing paradigm. Blockchain ensures secure, transparent, and
tamper-proof data management by validating and recording transactions via
consensus across network nodes. Federated Learning (FL), as a distributed
machine learning framework, enables participants to collaboratively train
models while safeguarding data privacy by avoiding direct raw data exchange.
Despite the growing interest in decentralized methods, their application in FL
remains underexplored. This paper presents a thorough investigation into
Blockchain-based FL (BCFL), spotlighting the synergy between blockchain's
security features and FL's privacy-preserving model training capabilities.
First, we present the taxonomy of BCFL from three aspects, including
decentralized, separate networks, and reputation-based architectures. Then, we
summarize the general architecture of BCFL systems, providing a comprehensive
perspective on FL architectures informed by blockchain. Afterward, we analyze
the application of BCFL in healthcare, IoT, and other privacy-sensitive areas.
Finally, we identify future research directions of BCFL.

区块链与联邦学习的综述和展望，介绍了基于区块链的联邦学习的分类、体系结构和应用，并指出了未来研究方向。

增强分布式网络中的信任和隐私：基于区块链的联邦学习的综合调查

Enhancing Trust and Privacy in Distributed Networks: A Comprehensive  Survey on Blockchain-based Federated Learning

Graph-based environments pose unique challenges to multi-agent reinforcement
learning. In decentralized approaches, agents operate within a given graph and
make decisions based on partial or outdated observations. The size of the
observed neighborhood limits the generalizability to different graphs and
affects the reactivity of agents, the quality of the selected actions, and the
communication overhead. This work focuses on generalizability and resolves the
trade-off in observed neighborhood size with a continuous information flow in
the whole graph. We propose a recurrent message-passing model that iterates
with the environment's steps and allows nodes to create a global representation
of the graph by exchanging messages with their neighbors. Agents receive the
resulting learned graph observations based on their location in the graph. Our
approach can be used in a decentralized manner at runtime and in combination
with a reinforcement learning algorithm of choice. We evaluate our method
across 1000 diverse graphs in the context of routing in communication networks
and find that it enables agents to generalize and adapt to changes in the
graph.

该研究论文探讨了在图形环境中基于图的多智能体强化学习所面临的挑战，并提出了一种通过与周围节点交换信息来创建全局图表示的循环传递信息模型，并在通信网络中的路由上验证了该方法，发现它使智能体能够泛化和适应图中的变化。

关于图中递归消息传递的多智能体强化学习的通用性

Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs  with Recurrent Message Passing

Stochastic bilevel optimization (SBO) is becoming increasingly essential in
machine learning due to its versatility in handling nested structures. To
address large-scale SBO, decentralized approaches have emerged as effective
paradigms in which nodes communicate with immediate neighbors without a central
server, thereby improving communication efficiency and enhancing algorithmic
robustness. However, current decentralized SBO algorithms face challenges,
including expensive inner-loop updates and unclear understanding of the
influence of network topology, data heterogeneity, and the nested bilevel
algorithmic structures. In this paper, we introduce a single-loop decentralized
SBO (D-SOBA) algorithm and establish its transient iteration complexity, which,
for the first time, clarifies the joint influence of network topology and data
heterogeneity on decentralized bilevel algorithms. D-SOBA achieves the
state-of-the-art asymptotic rate, asymptotic gradient/Hessian complexity, and
transient iteration complexity under more relaxed assumptions compared to
existing methods. Numerical experiments validate our theoretical findings.

通过引入单循环去中心化的 SBO（D-SOBA）算法，并建立其瞬态迭代复杂度，本文首次澄清了网络拓扑和数据异质性对去中心化双层算法的联合影响。与现有方法相比，D-SOBA 算法在更宽松的假设条件下实现了与现有方法相比最先进的渐近速率、渐近梯度 / 海森复杂度和瞬态迭代复杂度。数值实验验证了我们的理论发现。

图上分布式双层优化：无环算法更新和瞬态迭代复杂度

Decentralized Bilevel Optimization over Graphs: Loopless Algorithmic  Update and Transient Iteration Complexity

Multi-agent path planning is a challenging problem with numerous real-life
applications. Running a centralized search such as A* in the combined state
space of all units is complete and cost-optimal, but scales poorly, as the
state space size is exponential in the number of mobile units. Traditional
decentralized approaches, such as FAR and WHCA*, are faster and more scalable,
being based on problem decomposition. However, such methods are incomplete and
provide no guarantees with respect to the running time or the solution quality.
They are not necessarily able to tell in a reasonable time whether they would
succeed in finding a solution to a given instance. We introduce MAPP, a
tractable algorithm for multi-agent path planning on undirected graphs. We
present a basic version and several extensions. They have low-polynomial
worst-case upper bounds for the running time, the memory requirements, and the
length of solutions. Even though all algorithmic versions are incomplete in the
general case, each provides formal guarantees on problems it can solve. For
each version, we discuss the algorithms completeness with respect to clearly
defined subclasses of instances. Experiments were run on realistic game grid
maps. MAPP solved 99.86% of all mobile units, which is 18--22% better than the
percentage of FAR and WHCA*. MAPP marked 98.82% of all units as provably
solvable during the first stage of plan computation. Parts of MAPPs computation
can be re-used across instances on the same map. Speed-wise, MAPP is
competitive or significantly faster than WHCA*, depending on whether MAPP
performs all computations from scratch. When data that MAPP can re-use are
preprocessed offline and readily available, MAPP is slower than the very fast
FAR algorithm by a factor of 2.18 on average. MAPPs solutions are on average
20% longer than FARs solutions and 7--31% longer than WHCA*s solutions.

使用分散化方法，本研究引入了可操作的多智能體路徑規劃演算法 (MAPP)，並經實驗證明，其比現有的 FAR 和 WHCA * 方法解決率更高。