Cover Image

FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Han, Pengchao, Shi, Xingyan, Huang, Jianwei
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Convergence Analysis of Split Federated Learning on Heterogeneous Data
by: Han, Pengchao, et al.
Published: (2024)

Rethinking Knowledge Distillation in Collaborative Machine Learning: Memory, Knowledge, and Their Interactions
by: Han, Pengchao, et al.
Published: (2025)

Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
by: Yuan, Tianjun, et al.
Published: (2025)

DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
by: Zhu, Guogang, et al.
Published: (2024)

FedRIR: Rethinking Information Representation in Federated Learning
by: Huang, Yongqiang, et al.
Published: (2025)

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
by: Yi, Liping, et al.
Published: (2023)

DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning
by: Luo, Kangyang, et al.
Published: (2024)

FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference
by: Hou, Qijun, et al.
Published: (2026)

FedHB: Hierarchical Bayesian Federated Learning
by: Kim, Minyoung, et al.
Published: (2023)

Tackling Privacy Heterogeneity in Differentially Private Federated Learning
by: Xu, Ruichen, et al.
Published: (2026)

SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning
by: Liu, Xinyang, et al.
Published: (2024)

FedAgg: Adaptive Federated Learning with Aggregated Gradients
by: Yuan, Wenhao, et al.
Published: (2023)

FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
by: Feng, Zhiying, et al.
Published: (2023)

FedOptima: Optimizing Resource Utilization in Federated Learning
by: Zhang, Zihan, et al.
Published: (2025)

FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
by: Peng, Hongyi, et al.
Published: (2024)

UNIDEAL: Curriculum Knowledge Distillation Federated Learning
by: Yang, Yuwen, et al.
Published: (2023)

Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator
by: Luo, Kangyang, et al.
Published: (2024)

Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
by: Qin, Laiqiao, et al.
Published: (2024)

pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
by: Yi, Liping, et al.
Published: (2023)

FedSR: A Semi-Decentralized Federated Learning Algorithm for Non-IIDness in IoT System
by: Huang, Jianjun, et al.
Published: (2024)

Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
by: Li, Yi, et al.
Published: (2026)

FedZero: Leveraging Renewable Excess Energy in Federated Learning
by: Wiesner, Philipp, et al.
Published: (2023)

FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching
by: Yan, Qifan, et al.
Published: (2025)

Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data
by: Chen, Yiyue, et al.
Published: (2025)

FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
by: Liang, Han, et al.
Published: (2024)

FedMef: Towards Memory-efficient Federated Dynamic Pruning
by: Huang, Hong, et al.
Published: (2024)

FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
by: Chen, Yuanyuan, et al.
Published: (2022)

FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
by: Li, Shiwei, et al.
Published: (2024)

FedQHD: Closed-Form Function-Space Federated Reinforcement Learning
by: Hou, Yuchen, et al.
Published: (2026)

FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
by: Jhunjhunwala, Divyansh, et al.
Published: (2024)

FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration
by: Feng, Xue, et al.
Published: (2025)

FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences
by: Hartmann, Maria, et al.
Published: (2025)

FedRandom: Sampling Consistent and Accurate Contribution Values in Federated Learning
by: Geimer, Arno, et al.
Published: (2026)

Enhancing Split Learning with Sharded and Blockchain-Enabled SplitFed Approaches
by: Sokhankhosh, Amirreza, et al.
Published: (2025)

FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
by: Ilhan, Fatih, et al.
Published: (2025)

pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
by: Yi, Liping, et al.
Published: (2024)

FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)

FedAST: Federated Asynchronous Simultaneous Training
by: Askin, Baris, et al.
Published: (2024)

FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
by: Zhou, Liuzhi, et al.
Published: (2024)

FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
by: Li, Jian, et al.
Published: (2024)