## 7 Research Gaps and Future Directions
[119] study adversarial adaptations where model updates mimic benign behavior, bypassing current anomaly detection. Existing aggregation defenses like Krum [116] and Bulyan [126] struggle when attackers use model-aligned poisoning. [PERSON] et al. [124] propose using zero-knowledge proofs for secure update verification in FL, though integration into LLM systems is yet to be tested. Decentralized trust frameworks with verifiable integrity, such as those discussed in [42], could mitigate these threats in IoT federations. Standardization and Benchmarking: Most existing FL benchmarks are designed for small NLP tasks (e.g., FedNLP [98]), lacking scale and modality diversity. [PERSON] et al. [97] introduce FederatedGPT to benchmark instruction tuning under FL settings, incorporating metrics like alignment score and robustness. FederatedScope-LLM [96] goes further, providing end-to-end support for parameter-efficient tuning (e.g., LoRA, prompt tuning) across diverse datasets. However, neither covers streaming sensor data, nor evaluates under network constraints typical in IoT. A comprehensive benchmark must include multimodal tasks, model size variability, privacy/utility/fairness trade-offs, and realistic simulation environments [129]. Multimodal Federated Learning: IoT deployments naturally involve multimodal data. ImageBind [130] demonstrates crossmodal LLMs trained on image, audio, depth, and IMU inputs in a single embedding space, but assumes centralized training. [PERSON] et al. [105] highlight the challenges of decentralized multimodal alignment, including inter-client modality mismatch and unbalanced contributions. Communication-efficient multimodal fusion techniques and modality-specific adapters are needed. Sensor-based FL must incorporate asynchronous updates and crossmodal imputation to be practical in the real world. Federated Learning for AI Agents: [PERSON] et al. [131] envision LLM-based AI agents capable of perception, planning, and actuation across decentralized IoT systems. Such agents require lifelong learning and task adaptation, which traditional FL lacks. PromptFL [80] proposes learning shared prompts instead of entire models, while FedPrompt [81] enhances this with privacy-preserving prompt updates. These methods significantly reduce communication and allow client-specific behavior, but lack the reasoning and memory modules required by generalist agents. Integration with reinforcement FL and safe exploration policies is a future direction. Continual Learning and Adaptability: The temporal nature of IoT data leads to frequent concept drift. [PERSON] et al. [132] propose online adaptation techniques but do not consider privacy. [PERSON] et al. [107] review continual FL methods including regularization-based and rehearsal-based strategies. [PERSON] et al. [108] propose FCLLM-DT, which maintains temporal awareness via digital twins. These approaches should be enhanced with memory-efficient adaptation and forgettable modules that meet legal obligations on data deletion. Legal, Ethical, and Economic Considerations: Federated LLMs operating across jurisdictions must comply with evolving data governance policies. [PERSON] et al. [10] outline open legal questions in multi-party FL, such as liability for biased decisions and model misuse. [PERSON] et al. [13] emphasize ethical concerns such as disproportionate access to computing resources and biased training data. [PERSON] et al. [43] review incentive mechanisms like token-based payments or fairness-based credit allocation, critical for encouraging client participation. However, these are rarely tested in LLM-specific scenarios, and no consensus exists on equitable reward strategies. Machine Unlearning and Data Erasure: [PERSON] et al. [133] propose erasing LoRA-tuned knowledge via gradient projection and local retraining to remove specific client data contributions without damaging generalization. [PERSON] et al. [134] leverage influence functions to reduce a sample's effect on final predictions, but require full access to model internals. [PERSON] et al.