Federated Personalization of Early-Exit Networks

arXiv:2601.10015v2 Announce Type: replace Abstract: Personalized Federated Learning (PFL) excels at tailoring client-specific models, which is particularly critical for decentralized and heterogeneous data environments, yet existing methods produce static models with a fixed tradeoff between accuracy and efficiency. This inherent static nature limits their ability to adapt to inference demands that vary with context and resource availability, posing a challenge for real-world deployment. Early-exit networks (EENs), which enable adaptive inference via intermediate classifiers, offer a promising solution. However, integrating EENs into PFL introduces two intertwined conflicts: client-wise heterogeneity across clients and depth-wise interference arising from conflicting exit objectives. Prior studies fail to resolve both conflicts simultaneously, leading to suboptimal performance. In this paper, we propose X-FED, a novel Conflict-Aware Cross-Client Federated Exit Distillation framework that jointly addresses both client- and depth-wise conflicts while extending PFL to early-exit networks. At its core, X-FED employs a progressive, depth-prioritized student coordination mechanism that mitigates interference among shallow and deep exits while enabling effective personalized knowledge transfer across clients. Furthermore, we introduce a client-decoupled formulation that reduces communication overhead with theoretical soundness. Extensive evaluations on various datasets show that compared to state-of-the-art PFL and PFL-EE methods, X-FED achieves higher accuracy while reducing inference costs by 30.79%-46.86%.

Abstract:Personalized Federated Learning (PFL) excels at tailoring client-specific models, which is particularly critical for decentralized and heterogeneous data environments, yet existing methods produce static models with a fixed tradeoff between accuracy and efficiency. This inherent static nature limits their ability to adapt to inference demands that vary with context and resource availability, posing a challenge for real-world deployment. Early-exit networks (EENs), which enable adaptive inference via intermediate classifiers, offer a promising solution. However, integrating EENs into PFL introduces two intertwined conflicts: client-wise heterogeneity across clients and depth-wise interference arising from conflicting exit objectives. Prior studies fail to resolve both conflicts simultaneously, leading to suboptimal performance. In this paper, we propose X-FED, a novel Conflict-Aware Cross-Client Federated Exit Distillation framework that jointly addresses both client- and depth-wise conflicts while extending PFL to early-exit networks. At its core, X-FED employs a progressive, depth-prioritized student coordination mechanism that mitigates interference among shallow and deep exits while enabling effective personalized knowledge transfer across clients. Furthermore, we introduce a client-decoupled formulation that reduces communication overhead with theoretical soundness. Extensive evaluations on various datasets show that compared to state-of-the-art PFL and PFL-EE methods, X-FED achieves higher accuracy while reducing inference costs by 30.79%-46.86%.

Machine Learning (cs.LG)

arXiv:2601.10015 [cs.LG]

(or arXiv:2601.10015v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2601.10015 Focus to learn more arXiv-issued DOI via DataCite

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Federated Personalization of Early-Exit Networks

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