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Found 18 papers, 2 papers with code
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
Moreover, compared to synchronous FedPG, AFedPG improves the time complexity from $\mathcal{O}(\frac{t_{\max}}{N})$ to $\mathcal{O}(\frac{1}{\sum_{i=1}^{N} \frac{1}{t_{i}}})$, where $t_{i}$ denotes the time consumption in each iteration at the agent $i$, and $t_{\max}$ is the largest one.
Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration
In this paper, we propose consistency-guided temperature scaling (CTS), a new temperature scaling strategy that can significantly enhance the OOD calibration performance by providing mutual supervision among data samples in the source domains.
Decentralized Sporadic Federated Learning: A Unified Methodology with Generalized Convergence Guarantees
Decentralized Federated Learning (DFL) has received significant recent research attention, capturing settings where both model updates and model aggregations -- the two key FL processes -- are conducted by the clients.
Rethinking the Starting Point: Enhancing Performance and Fairness of Federated Learning via Collaborative Pre-Training
Most existing federated learning (FL) methodologies have assumed training begins from a randomly initialized model.
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
Multimodal federated learning (FL) aims to enrich model training in FL settings where clients are collecting measurements across multiple modalities.
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation
Federated learning (FL) is a promising approach for solving multilingual tasks, potentially enabling clients with their own language-specific data to collaboratively construct a high-quality neural machine translation (NMT) model.
Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading
While network coverage maps continue to expand, many devices located in remote areas remain unconnected to terrestrial communication infrastructures, preventing them from getting access to the associated data-driven services.
Submodel Partitioning in Hierarchical Federated Learning: Algorithm Design and Convergence Analysis
Hierarchical federated learning (HFL) has demonstrated promising scalability advantages over the traditional "star-topology" architecture-based federated learning (FL).
FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication
Multimodal federated learning (FL) aims to enrich model training in FL settings where devices are collecting measurements across multiple modalities (e. g., sensors measuring pressure, motion, and other types of data).
Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization
In domain generalization (DG), the target domain is unknown when the model is being trained, and the trained model should successfully work on an arbitrary (and possibly unseen) target domain during inference.
SplitGP: Achieving Both Generalization and Personalization in Federated Learning
A fundamental challenge to providing edge-AI services is the need for a machine learning (ML) model that achieves personalization (i. e., to individual clients) and generalization (i. e., to unseen data) properties concurrently.
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries
While federated learning (FL) allows efficient model training with local data at edge devices, among major issues still to be resolved are: slow devices known as stragglers and malicious attacks launched by adversaries.
Accelerating Federated Split Learning via Local-Loss-Based Training
Federated learning (FL) operates based on model exchanges between the server and the clients, and suffers from significant communication as well as client-side computation burden.
Few-Round Learning for Federated Learning
Of central issues that may limit a widespread adoption of FL is the significant communication resources required in the exchange of updated model parameters between the server and individual clients over many communication rounds.
FedMes: Speeding Up Federated Learning with Multiple Edge Servers
Our key idea is to utilize the devices located in the overlapping areas between the coverage of edge servers; in the model-downloading stage, the devices in the overlapping areas receive multiple models from different edge servers, take the average of the received models, and then update the model with their local data.
Sself: Robust Federated Learning against Stragglers and Adversaries
While federated learning allows efficient model training with local data at edge devices, two major issues that need to be resolved are: slow devices known as stragglers and malicious attacks launched by adversaries.
Communication-Computation Efficient Secure Aggregation for Federated Learning
Through extensive real-world experiments, we demonstrate that our scheme, using only $20 \sim 30\%$ of the resources required in the conventional scheme, maintains virtually the same levels of reliability and data privacy in practical federated learning systems.
Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks
Recent advances in large-scale distributed learning algorithms have enabled communication-efficient training via SignSGD.
