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Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
Multi-agent deep reinforcement learning: a survey
The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Decentralized federated averaging
Federated averaging (FedAvg) is a communication-efficient algorithm for distributed training
with an enormous number of clients. In FedAvg, clients keep their data locally for privacy …
with an enormous number of clients. In FedAvg, clients keep their data locally for privacy …
A unified theory of decentralized SGD with changing topology and local updates
Decentralized stochastic optimization methods have gained a lot of attention recently, mainly
because of their cheap per iteration cost, data locality, and their communication-efficiency. In …
because of their cheap per iteration cost, data locality, and their communication-efficiency. In …
Gradient surgery for multi-task learning
While deep learning and deep reinforcement learning (RL) systems have demonstrated
impressive results in domains such as image classification, game playing, and robotic …
impressive results in domains such as image classification, game playing, and robotic …
Robust aggregation for federated learning
We present a novel approach to federated learning that endows its aggregation process with
greater robustness to potential poisoning of local data or model parameters of participating …
greater robustness to potential poisoning of local data or model parameters of participating …
An overview of multi-agent reinforcement learning from game theoretical perspective
Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
Communication-efficient distributed learning: An overview
Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …
A survey of distributed optimization
In distributed optimization of multi-agent systems, agents cooperate to minimize a global
function which is a sum of local objective functions. Motivated by applications including …
function which is a sum of local objective functions. Motivated by applications including …