Adaptive Heterogeneous Compression for Resource-Efficient Federated Knowledge Distillation

• URL: http://arxiv.org/abs/2608.15660v1
• Date: Sun, 16 Aug 2026 10:08:26 GMT
• Title: Adaptive Heterogeneous Compression for Resource-Efficient Federated Knowledge Distillation
• Abstract summary: Federated learning (FL) enables privacy-preserving distributed model training but faces challenges from heterogeneous model architectures and limited communication resources at the network edge.<n>We propose a heterogeneous compression framework for FedKD that enables each client to select a compression strategy from a candidate set.<n>We show that ASCEND effectively adapts to heterogeneous model and resource settings, reducing communication overhead and training time while maintaining competitive model accuracy.
• Score: 15.300922734330271
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) enables privacy-preserving distributed model training but faces challenges from heterogeneous model architectures and limited communication resources at the network edge. Federated knowledge distillation (FedKD) alleviates model heterogeneity by combining prototype-wise parameter aggregation and knowledge transfer across heterogeneous models. However, transmitting gradients still introduces considerable communication overhead, while existing compression approaches typically apply a uniform strategy across clients and ignore their diverse model characteristics and resource capacities. To address this issue, we propose a heterogeneous compression framework for FedKD that enables each client to select a compression strategy from a candidate strategy set. We formulate the compression strategy selection problem as a non-stationary stochastic multi-armed bandit (MAB), where each arm corresponds to a compression strategy. An efficiency-aware reward is designed by jointly considering local optimization improvement, global knowledge alignment, and execution time. Based on this formulation, we develop an Adaptive heterogeneouS Compression algorithm for fEderated kNowledge Distillation (ASCEND), which employs an exponential moving average (EMA)-enhanced $ε$-greedy policy to balance exploration and exploitation. Experimental results on multiple datasets demonstrate that ASCEND effectively adapts to heterogeneous model and resource settings, reducing communication overhead and training time while maintaining competitive model accuracy.

Related papers

• Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards [16.135177543347773]
  Autoregressive (AR) models are highly effective for image generation, yet their standard maximum-likelihood estimation training lacks direct optimization for sample quality and diversity.<n>We propose a lightweight RL framework that casts token-based AR inference as a Markov Decision Process, optimized via Group Relative Policy Optimization.<n>Our core contribution is the introduction of a novel distribution-level Leave-One-Out FID (LOO-FID) reward.
  arXiv  Detail & Related papers  (2026-03-24T11:28:36Z)
• GO-GenZip: Goal-Oriented Generative Sampling and Hybrid Compression [50.76941829813458]
  Current network data telemetry pipelines consist of massive streams of fine-grained Key Performance Indicators (KPIs) from multiple distributed sources towards central aggregators.<n>This work presents a generative AI (GenAI)-driven sampling and hybrid compression framework that redesigns network telemetry from a goal-oriented perspective.
  arXiv  Detail & Related papers  (2026-03-20T16:33:15Z)
• Deep Leakage with Generative Flow Matching Denoiser [54.05993847488204]
  We introduce a new deep leakage (DL) attack that integrates a generative Flow Matching (FM) prior into the reconstruction process.<n>Our approach consistently outperforms state-of-the-art attacks across pixel-level, perceptual, and feature-based similarity metrics.
  arXiv  Detail & Related papers  (2026-01-21T14:51:01Z)
• Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients [60.22876915395139]
  This paper studies a challenging robust federated learning task with model heterogeneous and data corrupted clients.<n>Data corruption is unavoidable due to factors such as random noise, compression artifacts, or environmental conditions in real-world deployment.<n>We propose a novel Robust Asymmetric Heterogeneous Federated Learning framework to address these issues.
  arXiv  Detail & Related papers  (2025-03-12T09:52:04Z)
• FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling [11.913924455236652]
  Federated Learning (FL) enables collaborative model training across distributed clients without data sharing.<n>Current methods use dynamic pruning to improve efficiency by periodically adjusting sparse model topologies while maintaining sparsity.<n>We propose Federated Robust pruning via Thompson Sampling (FedRTS), a novel framework designed to develop robust sparse models.
  arXiv  Detail & Related papers  (2025-01-31T13:26:22Z)
• HeteroTune: Efficient Federated Learning for Large Heterogeneous Models [35.53420882449293]
  We propose HeteroTune, a novel federated fine-tuning paradigm for large, heterogeneous models operating under limited communication and budgets.<n>The core of our method lies in a novel architecture, DeMA, which enables flexible and efficient aggregation of heterogeneous models.<n>We provide both theoretical analysis and empirical evidence showing that HeteroTune achieves state-of-the-art performance and efficiency across diverse tasks and model architectures.
  arXiv  Detail & Related papers  (2024-11-25T09:58:51Z)
• Compressed Regression over Adaptive Networks [58.79251288443156]
  We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
  arXiv  Detail & Related papers  (2023-04-07T13:41:08Z)
• Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning [28.185096784982544]
  Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data. We propose a heterogeneous-aware FL framework, called FedCG, with adaptive client selection and gradient compression. Experiments on both real-world prototypes and simulations show that FedCG can provide up to 5.3$times$ speedup compared to other methods.
  arXiv  Detail & Related papers  (2022-12-19T14:19:07Z)
• Distributed Adaptive Learning Under Communication Constraints [54.22472738551687]
  This work examines adaptive distributed learning strategies designed to operate under communication constraints. We consider a network of agents that must solve an online optimization problem from continual observation of streaming data.
  arXiv  Detail & Related papers  (2021-12-03T19:23:48Z)
• Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data [125.7135706352493]
  Generative adversarial networks (GANs) typically require ample data for training in order to synthesize high-fidelity images. Recent studies have shown that training GANs with limited data remains formidable due to discriminator overfitting. This paper introduces a novel strategy called Adaptive Pseudo Augmentation (APA) to encourage healthy competition between the generator and the discriminator.
  arXiv  Detail & Related papers  (2021-11-12T18:13:45Z)
• Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization [21.81192774458227]
  One of the major bottlenecks is the large communication cost between the central server and the local workers. Our proposed distributed learning framework features an effective gradient gradient compression strategy.
  arXiv  Detail & Related papers  (2021-11-01T04:54:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.