Personalized Federated Fine-Tuning

Updated 16 October 2025

Personalized federated fine-tuning is a method that partitions model parameters into global and local components to tailor training to client-specific data distributions.
It employs techniques such as gradient-based subnetwork selection, adapters, and expert modules to optimize both personalization and communication efficiency.
This approach balances global aggregation with local adaptation, effectively addressing non-IID challenges and enhancing model performance in privacy-sensitive domains.

Personalized federated fine-tuning refers to a suite of methodologies in federated learning (FL) that aim to tailor model parameters or architectures to client-specific data distributions while retaining the benefits of collaborative training. Unlike standard federated learning, which produces a single global model aggregated across all clients, personalized approaches partition model adaptation to maximize both individual client performance and shared generalization, balancing communication efficiency, privacy, and heterogeneity management.

1. Key Principles and Problem Formulation

Personalized federated fine-tuning addresses the challenge that client data distributions are often highly heterogeneous (non-IID), leading to global models that may underperform on individual clients. The central principle is to decouple the adaptation of model parameters, architectural modules, or tuning strategies such that a shared/global component captures transferable knowledge, while a personalized/local component adapts to client-specific data.

This is commonly formalized by partitioning parameters into global (u) and personalized (vₖ) sets on each client k: $\theta_k = (u, v_k)$ and optimizing objectives such as: $\min_{u, \{v_k\}} \sum_k \frac{\alpha_k}{N_k} \sum_i f_k((u, v_k), x_i^k)$ where only u is aggregated globally and vₖ is adapted locally (Tamirisa et al., 2023). The scope of "personalization" ranges from fine-tuning classifier heads, subnetworks, or soft prompt matrices, to the architectural selection of adapters or expert modules.

2. Personalized Parameter/Module Selection Mechanisms

Several approaches have been developed to determine which parameters or components to personalize:

Gradient-based subnetwork selection (e.g., FedSelect, GradLTN): Parameters with the largest magnitude of local updates during training are earmarked for personalization. Binary masks are used to partition parameters, with the "active" ones fine-tuned locally while the rest inherit global knowledge and are aggregated (Tamirisa et al., 2023, Tamirisa et al., 3 Apr 2024).
Adapter and LoRA-based fine-tuning: Parameter-efficient fine-tuning (PEFT)—such as LoRA or adapter modules—enables injecting small, trainable modules into large pre-trained models. In personalized FL, each client either fine-tunes its own LoRA/adapters (which may remain local), while other shared adapters are globally aggregated (Jiang et al., 20 Apr 2024, Zhang et al., 28 Nov 2024).
Expert selection strategies (FedAMoLE): Instead of uniform architectures, clients are assigned different mixtures of "domain expert" modules (each implemented as LoRA adapters) based on the relevance between client data representations and expert embeddings. A global pool of experts and a token projection mechanism allow FedAvg-based aggregation even when client AMoLE modules vary in size (Zhang et al., 28 Nov 2024).
Rank-adaptive strategies (PF2LoRA, FedP²EFT): Instead of fixed-rank PEFT modules, clients use Bayesian or bilevel optimization to select the optimal rank (i.e., expressive power) of LoRA adapters per layer, based on the observed importance of each component on local data (Lee et al., 5 Feb 2025, Hao et al., 5 Mar 2025).
Bi-level and Mixture models: Some frameworks (e.g., bi-level task-vector aggregation, ensemble mixtures of federated and local models) use hierarchical adaptation: client-level fine-tuning followed by personalized aggregation using, e.g., task vector similarities, or ensembles weighted according to local and global model predictive performance (Ghari et al., 28 Oct 2024, Yang et al., 16 Sep 2025).

3. Aggregation, Communication, and Optimization

The aggregation and optimization strategy determines how global and personalized updates are synchronized and impacts the scalability and adaptation efficacy:

Selective Aggregation: Only the parameters marked as global (global subnetworks, shared adapters, or projection layers) are communicated and aggregated. Personalized parameters remain strictly local, reducing communication—especially as more parameters become personalized over training rounds (Tamirisa et al., 3 Apr 2024).
Personalized Aggregation Functions: Instead of simple averaging, personalized weights (based on data and parameter similarity) can be used to aggregate model updates, e.g., via affinity matrices constructed from Gaussian Mixture Model (GMM) or Centered Kernel Alignment (CKA) similarities (Li et al., 31 Mar 2025). Task-vector-based similarity weighting at the server enables only similar tasks to be aggregated together (Yang et al., 16 Sep 2025).
Tri-matrix and fine-grained adaptation: Tri-matrix/three-factor LoRA (A, C, B) restricts aggregation to a small, full-rank C matrix (e.g., r × r), dramatically lowering communication costs while maintaining model expressivity for local adaptation (Li et al., 31 Mar 2025).
Decoupled adaptation (FedALT, FedLoRA-Optimizer): Individual and global LoRA modules are trained separately (the "Rest-of-the-World" module is aggregated and held fixed during local updates), with dynamic gating (e.g., via a Mixture-of-Experts-style mixer) balancing local and global knowledge per input instance (Bian et al., 14 Mar 2025, Zhao et al., 13 Oct 2025). Fine-grained decomposition into "direction" (global knowledge) and "magnitude" (personalized information) components enables pipeline optimization—first aligning the direction globally, then specializing magnitude locally (Zhao et al., 13 Oct 2025).

4. Applications, Empirical Findings, and Trade-offs

Personalized federated fine-tuning underpins applications in NLP, vision, and multi-modal settings, especially for:

Resource-constrained or privacy-sensitive domains: Healthcare (medical imaging federations), mobile devices, and cross-silo cross-domain scenarios benefit from personalized FL, as direct data sharing is infeasible and data heterogeneity is high (Tupper et al., 14 Oct 2025).
Large/complex foundation models: Adapter-based and split learning approaches make the deployment of large foundation models practical on resource-limited devices by reducing both computational and communication loads (Jiang et al., 20 Apr 2024, Yuan et al., 14 Aug 2025).

Empirical findings demonstrate:

Approach	Key Benefit	Limitation
Subnetwork selection (FedSelect)	Fine-grained personalization; efficient	Parameter selection hyperparameters (p, α)
LoRA/Adapter-based PEFT	Low overhead, privacy respecting	May underfit if adapter capacity is too small
Heterogeneous architectures	Better fit for diverse clients	Assignment/optimization complexity
Personalized aggregation	Robust to data heterogeneity	Extra computation for similarity calculation
Bi-level/Mixture strategies	Fast convergence, avoids overfitting	Higher memory/storage for ensembles

Personalization–generalization trade-off: Extensive studies (e.g., (Collins et al., 2023)) show that high personalization often comes at the expense of global robustness. Clients can avoid catastrophic forgetting via adaptive learning rates, regularization (e.g., ℓ₂ penalty), or model interpolation (averaging global and personalized solutions).
Scalability and efficiency: Communication-efficient methods (e.g., CE-LoRA, partial aggregation, split learning) enable deployment at scale, reducing communication rounds and bandwidth requirements (Li et al., 31 Mar 2025, Yuan et al., 14 Aug 2025).
Privacy and security: Limiting communication to adapter or tri-matrix parameters (rather than full gradients) reduces the vulnerability to gradient-based data reconstruction attacks (Li et al., 31 Mar 2025).

5. Extensions, Open Challenges, and Future Directions

Several open challenges and research frontiers are highlighted in recent works:

Adaptive personalization control: Automatically adjusting personalization hyperparameters (e.g., mask size, adapter rank, alignment λ) based on client complexity and learning progress can improve convergence and client utility (Tamirisa et al., 2023, Tamirisa et al., 3 Apr 2024).
Handling adversarial clients and robust aggregation: Interpolated objectives (balancing local and global loss with λ) and robust aggregators (e.g., excluding or downweighting adversarial updates) resist adversarial disruptions. Theoretical analyses elucidate the interplay among heterogeneity, adversary fraction, and optimal collaboration level (Allouah et al., 30 Sep 2024).
Test-time adaptation and task uncertainty: Architectures such as dual-personalizing adapters (Yang et al., 28 Mar 2024) dynamically combine global and local modules via instance-wise weighting at inference, directly tackling distribution shifts between training and deployment distributions.
Layer/architecture-level personalization: Methods that enable heterogeneous architectures, where each client may use a different set of experts or adapter modules, require advanced routing and aggregation logic but show strong gains on highly diverse tasks (Zhang et al., 28 Nov 2024).
Broader modalities and applications: Recent advances support federated fine-tuning for foundation models in vision-language tasks (Ghiasvand et al., 7 Jul 2025, Mitra et al., 23 Jul 2025), online streaming settings (Ghari et al., 28 Oct 2024), and personalized healthcare models (Tupper et al., 14 Oct 2025).

6. Methodological Diversity and Comparative Insights

Recent empirical meta-analyses (Khan et al., 10 Sep 2024) systematically compare multiple personalization strategies, classifying them into categories such as:

Fine-tuning methods (single model adaptation): Low resource usage but limited in handling severe heterogeneity.
Multi-objective methods (e.g., Ditto-style balancing): Higher accuracy on non-IID data but increased computation and memory demands.
Personalized aggregation methods (e.g., FedALA, adaptive weighted averaging): Fast convergence and robust adaptation, favored in large-scale deployments.

Decisions among these methods depend on deployment constraints, desired trade-offs among memory, communication cost, convergence speed, and adversarial robustness.

7. Significance in Real-World and Future Federated AI Systems

Personalized federated fine-tuning is recognized as pivotal for:

Enabling task-specific adaptation of foundation models without compromising data privacy or requiring global data sharing.
Making advanced models accessible on-device or in sensitive environments (e.g., medical/edge/IoT).
Balancing the utility of collaborative knowledge while respecting personalization, resource constraints, and privacy.
Supporting practical deployments through communication-efficient, scalable, and robust techniques.

Ongoing research directions include extending these methods to more diverse modalities, refining architecture adaptation, adaptive parameterization of personalization, and improved theoretical analysis of personalization–federation trade-offs. Addressing the challenges of adversarial robustness, quantifying the cost–benefit of various partitioning strategies, and harmonizing automatic model selection with resource-awareness remain open problems for the domain.