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Communication and computation efficiency in federated learning: A survey
Federated Learning is a much-needed technology in this golden era of big data and Artificial
Intelligence, due to its vital role in preserving data privacy, and eliminating the need to …
Intelligence, due to its vital role in preserving data privacy, and eliminating the need to …
Enabling federated learning across the computing continuum: Systems, challenges and future directions
In recent years, as the boundaries of computing have expanded with the emergence of the
Internet of Things (IoT) and its increasing number of devices continuously producing flows of …
Internet of Things (IoT) and its increasing number of devices continuously producing flows of …
Fairness and privacy preserving in federated learning: A survey
Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …
addresses privacy concerns by allowing participants to collaboratively train machine …
Federated reinforcement learning: Linear speedup under markovian sampling
Since reinforcement learning algorithms are notoriously data-intensive, the task of sampling
observations from the environment is usually split across multiple agents. However …
observations from the environment is usually split across multiple agents. However …
Every parameter matters: Ensuring the convergence of federated learning with dynamic heterogeneous models reduction
Abstract Cross-device Federated Learning (FL) faces significant challenges where low-end
clients that could potentially make unique contributions are excluded from training large …
clients that could potentially make unique contributions are excluded from training large …
Adaptive incentive for cross-silo federated learning in IIoT: a multiagent reinforcement learning approach
In the Industrial Internet of Things (IIoT), cross-silo federated learning (CSFL) enables
entities, such as manufacturers and suppliers to train global models for optimizing …
entities, such as manufacturers and suppliers to train global models for optimizing …
Float: Federated learning optimizations with automated tuning
Federated Learning (FL) has emerged as a powerful approach that enables collaborative
distributed model training without the need for data sharing. However, FL grapples with …
distributed model training without the need for data sharing. However, FL grapples with …
Spherefed: Hyperspherical federated learning
Federated Learning aims at training a global model from multiple decentralized devices (ie
clients) without exchanging their private local data. A key challenge is the handling of non …
clients) without exchanging their private local data. A key challenge is the handling of non …
Stochastic distributed optimization under average second-order similarity: Algorithms and analysis
We study finite-sum distributed optimization problems involving a master node and $ n-1$
local nodes under the popular $\delta $-similarity and $\mu $-strong convexity conditions …
local nodes under the popular $\delta $-similarity and $\mu $-strong convexity conditions …
FedRL: a reinforcement learning federated recommender system for efficient communication using reinforcement selector and hypernet generator
The field of recommender systems aims to predict users' latent interests by analyzing their
preferences and behaviors. However, privacy concerns about user data collection lead to …
preferences and behaviors. However, privacy concerns about user data collection lead to …